Статті в журналах: "Early Detection of Parkinson's Disease"

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Kiruthika, S. "The Parkinson’s Puzzle: Early Detection & Diagnosis." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 01 (January 22, 2025): 1–9. https://doi.org/10.55041/ijsrem41001.

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Parkinson's disease is a neurological disorder that affects movement and becomes progressively worse over time. Currently, there is no cure for the disease, but early detection and appropriate management can significantly improve the patient's quality of life. This project presents a web-based application designed to detect Parkinson's disease based on the analysis of audio recordings of patient's voices. The application utilizes a machine learning model that has been trained on a dataset consisting of audio recordings from both Parkinson's disease patients and healthy individuals. The web application extracts relevant features from the audio recordings, employing machine learning algorithms to predict the probability of Parkinson's disease. The machine learning models used in this research are Logistic Regression, Linear Discriminant Analysis, K-Neighbors Classifier, MLP Classifier, Gaussian NB, XGB Classifier, Random Forest Classifier, and Cat Boost Classifier and stacked three models (XG Boost, Random Forest, and Cat Boost) using Stacking CV Classifier out of which the model with the highest performance will be chosen to develop the web application. Keywords: Parkinson’s Disease, Web Application, Ensemble Learning, Stacked Model

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N., Chandana, Divya C. D., and Radhika A. D. "A Review on Parkinsons Disease Detection." Applied and Computational Engineering 2, no. 1 (March 22, 2023): 760–65. http://dx.doi.org/10.54254/2755-2721/2/20220675.

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Parkinson's Disease affects hundreds of thousands of people . Even with today's many sciences and advances, detecting this disorder in early stage remains a challenge. The detection of any neurological disorder could be critical. Various tools and techniques are now available on a global scale. These strategies are primarily built on mobile ,web-based utility .These strategies are also user-friendly because disease caregivers can use them while sitting at home and can display disease progression. Clinicians and researchers who want to conduct research also use these applications to display disease progressions. According to the current information review, certain algorithms have been used to obtain good results, In this case, the difficulty is defining the utmost effective classifier for Parkinsons Disease detection. Also emphasises a organized assessment of several styles of programmes employed globally for Parkinson's disease detection , prognosis, proposes a Parkinson's disease detection system that is entirely based on mobile and internet utility.

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Irin Akter Liza, Ekramul Hasan, Md Musa Haque, Shah Foysal Hossain, Md Al Amin, and Shahriar Ahmed. "Predictive Modeling and Early Detection of Parkinson's Disease Using Machine Learning." Journal of Medical and Health Studies 5, no. 4 (November 12, 2024): 97–107. http://dx.doi.org/10.32996/jmhs.2024.5.4.12.

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Parkinson's disease is a progressive neurodegenerative disorder that impacts millions of citizens in the USA, mainly targeting the motor system and causing debilitating symptoms such as rigidity, tremors, and bradykinesia. The diagnosis of Parkinson's disease is presently heavily dependent on clinical evaluations and neurological examinations, targeting the detection of motor dysfunction. The principal aim of this study was to use machine learning as a means for early detection and prediction of Parkinson's disease. The dataset utilized for this study was the Parkinson’s Disease Dataset, retrieved from the UCI Machine Learning Repository, which included comprehensive biomedical voice measurements from a cohort of individuals, 23 of whom are diagnosed with Parkinson’s disease and 8 who are healthy controls. This dataset included a set of features extracted from voice recordings. These include parameters like fundamental frequency (pitch), amplitude variation, jitter, shimmer, and several phonation-related measures known to reflect early vocal impairments associated with the disease in question. This research project deployed three credible and proven algorithms, namely, logistic regression, random forest, and the Support Vector Machines. Besides, this study employed a combination of metrics, including accuracy, precision, recall, F1-score, and ROC-AUC, which are more holistic toward the model performance. According to the metric performance results of the three models, several key insights were drawn into the early detection of Parkinson's Disease. Particularly, it was clear that the Random Forest model had superior accuracy and was the most reliable in classifying positive cases and healthy patients, which could be that this model turned out to be most reliable in an early detection setting. In that respect, predictive modeling in Parkinson's Disease is a capability frontier that has the potential to make much difference in clinical decision-making. Supported by Machine Learning algorithms, clinicians can trace the minute patterns in patient data, which are not easily visible through any other diagnostic means. In such cases, early detection of Parkinson's Disorder with vocal biomarkers or motor assessment may afford healthcare professionals opportunities for early interventions that might retard the disease process.

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Kavitha Soppari, Bharath Vupperpally, Harshini Adloori, Kumar Agolu, and Sujith kasula. "AI-powered early detection of neurological disease: Parkinson's disease." International Journal of Science and Research Archive 14, no. 1 (January 30, 2025): 278–82. https://doi.org/10.30574/ijsra.2025.14.1.0041.

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Parkinson's disease (PD), a neurological illness that gradually compromises motor abilities. Tremors, muscle rigidity, and bradykinesia (slowness of movement) are symptoms of PD. Effective therapy depends on a prompt and accurate diagnosis, yet traditional diagnostic methods can be laborious and subjective. The goal of this study is to create a machine learning-based model that uses clinical information, like vocal characteristics, to detect Parkinson's disease. Through the use of advanced machine learning algorithms and the extraction of important data patterns, the project hopes to develop a trustworthy diagnostic tool that will help physicians identify Parkinson's disease (PD) early on, facilitating quicker interventions and improved patient care.

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S, Rohan, R. Subrahmanya, and Vignesh M. "Parkinson’s Disease Detection using YOLO Algorithm." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 12 (December 30, 2024): 1–9. https://doi.org/10.55041/ijsrem40409.

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Parkinson's disease (PD) is a progressive neurodegenerative disorder characterized by motor and non- motor symptoms, which significantly impact patients' quality of life. Early and accurate diagnosis is important for effective management and treatment planning. Recent advancements in deep learning techniques, particularly with the You Only Look Once (YOLO) algorithm, have shown promise in enhancing medical imaging analysis and automated diagnosis. This study explores the use of YOLO, a real-time object detection algorithm, for identifying Parkinson’s disease-related abnormalities in medical images such as MRI and PET scans. The YOLO framework is modified and trained to detect specific biomarkers and structural changes associated with PD in imaging data, which allows for rapid and accurate identification of disease patterns. Results demonstrate that YOLO’s high-speed detection and localization capabilities make it suitable for processing large volumes of medical data, offering an efficient and cost- effective solution for PD screening. This application of YOLO in PD detection may improve early diagnosis, aid in monitoring disease progression, and support the development of individualized treatment strategies. Further research is needed to refine the model for greater accuracy and clinical applicability across diverse patient populations. Keywords—YOLO, F1 Score, Parkinson’s Disease, Convolutional Neural Networks, Neurodegenerative disorder, Parkinson's Progression Markers Initiative (PPMI).

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Samita Ganveer, Himani Bire, Rutuja Deshmukh, Shweta S. Salunkhe,. "Early Detection of Parkinson’s Disease Using Machine Learning." Journal of Electrical Systems 20, no. 2 (April 4, 2024): 2255–66. http://dx.doi.org/10.52783/jes.1992.

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A hallmark of Parkinson's disease is the degeneration of dopaminergic neurons in the midbrain's substantia nigra pars compacta. However, machine learning is needed to design and implement early Parkinson's disease detection. by using machine learning methods such as CNN and SVM, which can reliably identify voice signals and spiral images to identify early indications of Parkinson's disease. Using machine learning on handwriting, tremor, and gait datasets, the method addresses the shortcomings of individual analyses for a more complete diagnosis solution by investigating relationships between symptoms. This increases accuracy. When voice and spiral drawing data were combined, Parkinson's disease diagnosis accuracy showed promise, with the machine learning model successfully differentiating between unaffected patients and those who were affected. This observation suggests a viable path for precise Parkinson's disease diagnosis: an integrated method that combines machine learning techniques with data from spiral drawings and voice.

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Adekunle, Abiona Akeem, Oyerinde Bolarinwa Joseph, and Ajinaja Micheal Olalekan. "Early Parkinson's Disease Detection Using by Machine Learning Approach." Asian Journal of Research in Computer Science 16, no. 2 (June 9, 2023): 36–45. http://dx.doi.org/10.9734/ajrcos/2023/v16i2337.

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Parkinson's disease (PD) is a progressive neurodegenerative disorder that affects movement and motor skills. Early diagnosis and treatment of Parkinson's disease are crucial for improving patient outcomes; however, traditional diagnostic methods are time-consuming and subject to observer bias. This study aims to use a machine learning model for the detection of Parkinson's disease. The model will be trained on a public repository dataset of biomedical voice measurements from individuals with and without Parkinson's disease and its performance will be evaluated in terms of accuracy and precision. The results of this study have the potential to revolutionize the diagnosis of Parkinson's disease by providing a fast, non-invasive, and reliable diagnostic tool. The study's results could also have implications for the development of similar diagnostic tools for other neurodegenerative disorders.

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Montgomery, Erwin B. "Olfaction and early detection of Parkinson's disease." Annals of Neurology 57, no. 1 (2004): 157. http://dx.doi.org/10.1002/ana.20354.

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I, Kalaiyarasi, Amudha P, and Sivakumari S. "Parkinson\'s Disease Detection Using Deep Learning Technique." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (May 31, 2023): 1789–96. http://dx.doi.org/10.22214/ijraset.2023.51916.

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Abstract: Parkinson’s Disease is a degenerative nervous system ailment primarily impacting middle-aged and older persons. Tremors, stiffness of the muscles, and slow, clumsy movement mark it. Parkinson's Disease is believed to be caused by genetic and environmental factors, while its precise cause is yet unclear. Levodopa can aid patients' quality of life and manage their symptoms, but there is no proven treatment for Parkinson's Disease. A unique deep-learning approach is developed to determine whether a person has PD based on premotor traits. This study has looked explicitly at several signs to identify PD at an early stage using spiral drawing. Measuring the changes in the handwritten spiral drawing allows for the early and accurate diagnosis of PD. Deep learning algorithms have been used to track the development of the illness and the effectiveness of treatment in PD patients in addition to diagnosis and prediction. By utilizing efficient treatments and medications, the findings will showcase how early illness detection can enhance a patient's life expectancy and enable them to live peacefully. One commonly utilized deep learning technique in PD research is Convolutional Neural Networks (CNNs). This work uses Modified Convolutional Neural Networks (MCNN) to predict the normal and abnormal of Parkinson's Disease. The complete model's performance after being trained on data from 36 patients was 96% overall accuracy, with average recall, precision, and f1 scores of 96.73%, 96.05%, and 96%, respectively.

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Generaldo Maylem, Genica Lynne Maylem, Isaac Angelo M. Dioses, Loida Hermosura, James Bryan Tababa, Aldrin Bryan Tababa, Marc Zenus Labuguen, and Dave Miracle Cabanilla. "Speech-based biomarkers for Parkinson’s disease detection and classification using AI Approach." World Journal of Advanced Research and Reviews 25, no. 2 (February 28, 2025): 2127–33. https://doi.org/10.30574/wjarr.2025.25.2.0595.

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Parkinson's disease (PD) is a progressive neurological condition that impairs motor and speech function. Early and precise detection is critical for prompt intervention and illness treatment. This work applies machine learning approaches to classify Parkinson's disease using speech biomarkers collected from voice recordings. The dataset includes a variety of acoustic parameters that capture speech anomalies often seen in people with Parkinson's disease. The Chi-Square (Chi2) approach was used to pick the most important predictors, which improved model performance and reduced computational complexity. The fine K-Nearest Neighbors (KNN) classifier was implemented, achieving a validation accuracy of 74.7%. The model demonstrated a moderate ability to distinguish between Parkinson’s and non-Parkinson’s cases, as indicated by an area under the curve (AUC) score of 0.7421. However, the confusion matrix revealed challenges in misclassification, with false positives leading to potential unnecessary medical evaluations and false negatives resulting in missed diagnoses. This study highlights the potential of machine learning in Parkinson’s detection while emphasizing the need for further refinement to enhance classification accuracy

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G, Himaja, Nagarathna C R, Jayasri A, and Kundan K M. "Prediction of Parkinson’s Disease using Handwriting Analysis and Voice Dataset- A Review." June 2024 6, no. 2 (June 2024): 118–32. http://dx.doi.org/10.36548/jiip.2024.2.004.

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Parkinson's disease is a common neurological movement illness that impairs motor coordination. Parkinson’s disease (PD) symptoms and severity, however, differ from person to person. By extracting insights, trends, and possibilities from the data, data research can be utilized to uncover solutions to problems in medical research by utilizing data, machine learning algorithms, and cutting-edge technology. Among the less evident early signs of Parkinson's disease are tremors, muscle stiffness, imbalance problems, and difficulty walking. There is currently no test to detect the illness early on, when symptoms might not be evident. However, handwriting and hand- drawn subjects in humans have been linked to PD. In addition to being a useful tool for PD prediction, speech smearing functions as an early warning system. In order to control symptoms and maybe halt the disease's progression, early detection makes it possible to organize treatments and intervene promptly. For those with Parkinson's disease, early application of certain therapies and medications can extend survival and enhance quality of life.

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Govindu, Aditi, and Sushila Palwe. "Early detection of Parkinson's disease using machine learning." Procedia Computer Science 218 (2023): 249–61. http://dx.doi.org/10.1016/j.procs.2023.01.007.

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Matmurodov, Rustambek, and Hilola Daminova. "Early detection of Parkinson's disease in ambulatory conditions." Journal of the Neurological Sciences 455 (December 2023): 121777. http://dx.doi.org/10.1016/j.jns.2023.121777.

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Devi, G. Naga Rama, and V. Rama Raju. "Significancy of human motor tasks during dual gate execution for uncovering Parkinson disease early." IP Indian Journal of Neurosciences 10, no. 3 (October 15, 2024): 157–63. http://dx.doi.org/10.18231/j.ijn.2024.034.

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Parkinson’s, i.e., Parkinson diseased (PD) patients appear beyond decreased gait execution during motor dual cognitive task tests. Yet, the impact of motor cognition task difficulty in early detection of PD has not been seen scientifically. the purpose is to detect the PD very early during the gait implementation of motor`s dual-tsks. Twenty-five advanced idiopathic Parkinson`s also fourteen healthy controls recruited in this study. As per the neuroscientist, all must complete a composite motor-task with and without 3 distinct mental-tasks. Based on spatiotemporal gait parameters plus joint-kinematics, the interventional composite issue features were computed. The outcome of task complexity plus cohort over the complex task interference (CTI) was studied first with the continual (repetitive) measures analysis-of-variance (ANOVA). Support vector machine (SVM)-based classifiers of Parkinson`s were constructed based on characterized features-of CTI. Our findings showed that the complexity of motor-issue has had a larger impact over gait accomplishment that much contributed to the advanced precision in categorizing Parkinson`s. The set with accuracy (97.7%), precision (98.9%), and recall(97.7%) was attained best. This study showed the application of a rotary-based motor`s dual task cognition idea of test in clinical settings to detect PD early is great.This study investigated a new method for early detection of Parkinson's disease (PD) using a dual-task test with cognitive and motor components.

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M, Sakshi, Dr Sankhya N. Nayak, Skanda G N, Shreyas R. Adiga, and Pavana R. "A Review on Detection of Parkinsons Disease Using ML Algorithms." International Journal for Research in Applied Science and Engineering Technology 11, no. 3 (March 31, 2023): 696–99. http://dx.doi.org/10.22214/ijraset.2023.49497.

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Abstract: Parkinson disease prediction is an area of active research in healthcare and machine learning. Even though Parkinson's disease is not well-known worldwide, its negative impacts are detrimental and should be seriously considered. Furthermore, because individuals are so immersed in their busy lives, they frequently disregard the early signs of this condition, which could worsen as it progresses. There are many techniques for Parkinson disease prediction. In this paper we are going to discuss some of the possible technical solutions proposed by researchers

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Lamba, Rohit, Tarun Gulati, and Anurag Jain. "An Intelligent System for Parkinson's Diagnosis Using Hybrid Feature Selection Approach." International Journal of Software Innovation 10, no. 1 (January 2022): 1–13. http://dx.doi.org/10.4018/ijsi.292027.

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Parkinson’s is the second most common neurodegenerative disorder after Alzheimer’s disease which adversely affects the nervous system of the patients. During the nascent stage, the symptoms of Parkinson’s disease are mild and sometimes go unnoticeable but as the disease progresses the symptoms go severe, so its diagnosis at an early stage is not easy. Recent research has shown that changes in speech or distortion in voice can be taken effectively used for early Parkinson’s detection. In this work, the authors propose a system of Parkinson's disease detection using speech signals. As the feature selection plays an important role during classification, authors have proposed a hybrid MIRFE feature selection approach. The result of the proposed feature selection approach is compared with the 5 standard feature selection methods by XGBoost classifier. The proposed MIRFE approach selects 40 features out of 754 features with a feature reduction ratio of 94.69%. An accuracy of 93.88% and area under curve (AUC) of 0.978 is obtained by the proposed system.

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Nasution, Amalia Noor Zafira, Aldy S. Rambe, and Haflin Soraya Hutagalung. "Relation between Parkinson's Disease Severity and Cognitive Function with Monstreal Cognitive Assessment Indonesia." Journal of Society Medicine 2, no. 9 (September 30, 2023): 296–301. https://doi.org/10.47353/jsocmed.v2i9.86.

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Introduction: Parkinson disease is a wide-spectrum disease that can be accompanied by motor and non-motor symptoms. Non-motor symtoms can occurred before the existance of motoric symptoms until the terimal stage of the disease, where cognitive disturbance is one of the non-motor symptoms that can decrease patient’s quality of life and increase patient’s disability. Therefore, early detection of the cognitive function is important for patients with parkinson disease. The aim of this study was to find the association between the severity of Parkinson’s disease and cognitive disturbance using the Montreal Cognitive Assesment Indonesian version (MoCA-Ina) Method: This study used cross-sectional design. The research subject was a parkinson disease patients’ who went to Neurology Clinic at Haji Adam Malik General Hospital Medan and network hospital who met the inclusion and exclusion criteria of the study. The number of sample was 39 subjects. To determine the relationship between the severity of parkinson disease and cognitive function, the Gamma test was used. Results: There was a significant correlation between the severity of Parkinson's disease and cognitive function (p = 0.001, r = -0.858). There was a very strong correlation between the severity of Parkinson's disease and cognitive function, and the negative correlation means the higher the severity of disease, the lower the cognitive function. From this study, the most correlated domains were delayed memory, naming (r = 0.962), orientation (r = -0.944), visuospatial (r = -0.929), abstraction (r = -0.874), language (r = -0.674), attention (r = -0.592). Delayed memory could not be statistically analyzed because delayed memory were all impaired in all subjects. Conclusion: There was a correlation between the severity of Parkinson's disease and cognitive function with a very strong correlation strength. The cognitive function domains that correlate strongly with Parkinson's severity were delayed memory, naming, orientation, visuospatial and abstraction.

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Shreeram Sanjay Sawant, Rahul Satheesan Nair, and Dr. Rohini Patil. "Machine Learning Techniques for Early Detection of Parkinson's disease." International Journal of Scientific Research in Science and Technology 12, no. 2 (March 4, 2025): 134–43. https://doi.org/10.32628/ijsrst2512169.

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This study explores the application of advanced machine learning techniques for diagnosing Parkinson's Disease (PD) using a comprehensive dataset of 195 records, which includes attributes such name, MDVP:Fo(Hz), MDVP:Fhi(Hz), MDVP:Flo(Hz) , MDVP:Jitter(%), MDVP:Jitter(Abs), MDVP:RAP , MDVP:PPQ, Jitter:DDP, MDVP:Shimmer, MDVP:Shimmer(dB), Shimmer:APQ3, Shimmer:APQ5 , MDVP:APQ , Shimmer:DDA, NHR , HNR , status , RPDE , DFA, spread1, spread2 , D2 and PPE. Various algorithms—including Logistic Regression, Support Vector Machine, Random Forest, and XGBoost—were employed to identify the most effective model for accurately predicting calories burned. The results indicate that XGBoost outperforms the other models, achieving the highest accuracy of 97.96% and AUC score, while revealing significant insights into how specific vocal features contribute to the diagnosis of PD. This research underscores the potential of machine learning for improving early detection and management of Parkinson's Disease, offering personalized diagnostic insights that enhance clinical decision-making and patient care.

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Rifqah Fahira, Nurul, Armin Lawi, and Masjidil Aqsha. "Early detection model of Parkinson's Disease using Random Forest Method on voice frequency data." Journal of Natural Sciences and Mathematics Research 9, no. 1 (December 13, 2023): 29–37. http://dx.doi.org/10.21580/jnsmr.2023.9.1.13148.

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Parkinson's disease is the most common nervous system disease that affects all ethnicities, genders, and ages, with a higher prevalence in the elderly and men. Developing countries tend to have higher cases of Parkinson's. The prevalence of death due to Parkinson's in Indonesia reaches the fifth highest cases in Asia and 12th in the world. This neurodegenerative disease affects a person's ability to control movement. Currently, the diagnosis of Parkinson's disease is only based on observation of motor symptoms. Therefore, early detection of the disease cannot be done. His paper proposes an efficient way to detect Parkinson's disease symptoms by comparing the fundamental frequencies of patients' voices using the random forest method. Random forest is a Machine Learning method that applies the ensemble concept, which aims to improve the performance of the classification by combining several decision trees as a basis. Random forests have shown superior algorithm performance in numerous health studies. In this study, the dataset consisted of 20 patients with Parkinson's and 20 normal patients. Data for each patient was taken from 26 types of voice records, and thus, the total data was 1,040 observations. The obtained data is prepared by filtering and rescaling. Then, the data is split and modelled using the Random Forest Method. The random forest model obtained accuracy results of 72.50%, precision (normal) of 72.28%, precision (Parkinson's) of 72.73%, sensitivity (normal) of 73.00%, sensitivity (Parkinson's) of 72.00% and AUC is 80.70%. The built random forest model is quite good at Parkinson's disease detection.

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Badhan, Pawan Kumar, and Manjeet Kaur. "Early Detection of Parkinson Disease Throughbiomedical Speech and Voice Analysis." International Journal on Soft Computing, Artificial Intelligence and Applications 13, no. 1 (February 28, 2024): 11–22. http://dx.doi.org/10.5121/ijscai.2024.13102.

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Parkinson's disease, widely recognized as a neurodegenerative condition characterized by subtle changes in voice, has spurred an investigation into voice analysis for diagnostic purposes. This study is dedicated to the early detection of Parkinson's disease through a comprehensive examination of biomedical speech attributes. Parameters such as fundamental frequency range, jitter, shimmer, noise-to-harmonics ratio, and features derived from nonlinear analysis are considered, alongside variables like status, indicating the presence of neurological disorders, and class for classification purposes. Together, these attributes provide a detailed representation of voice signals, offering valuable insights into both neurological and voice disorders for research purposes. The dataset exhibits promising potential for applications in medical diagnostics and voice analysis. In the pursuit of accurate disease detection, various machine learning methodologies are employed, including Support Vector Machines (SVM), Random Forest (RF), Decision Tree (DT), Neural Networks (NN), and state- of-the-art Convolutional Neural Networks (CNNs). The incorporation of CNNs is pivotal, signifying a significantleap in accuracy of 100%for disease detection. The results showcase a model adept at discerning subtle changesassociated with Parkinson's disease, with SVM achieving 96%, Decision Tree demonstrating a perfect 100%, Neural Network attaining 98%, and Random Forest showcasing an accuracy of 99%. This innovative approach not only transforms early Parkinson's disease identification through voice analysis, setting a precision benchmark, but also underscores the transformative potential of cutting-edge technologies in healthcare practices. The study positions the model as a reliable diagnostic tool, capable of advancing medical diagnosticsthrough the seamless integration of biomedical research and machine learning, contributing to the broader fieldof neurodegenerative disease diagnostics.

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Senarath Yapa, S. C. "Detection of subclinical ascorbate deficiency in early Parkinson's disease." Public Health 106, no. 5 (September 1992): 393–95. http://dx.doi.org/10.1016/s0033-3506(05)80188-x.

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Jobbagy, A., E. Furnee, P. Harcos, and M. Tarczy. "Early detection of Parkinson's disease through automatic movement evaluation." IEEE Engineering in Medicine and Biology Magazine 17, no. 2 (1998): 81–88. http://dx.doi.org/10.1109/51.664035.

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S. Sasi Rekha, Dr. R Shankar, and Dr. S. Duraisamy. "Parkinson's illness Deep Learning Diagnosis: An Innovative LSTM-Based Method for Freezing Gait Detection." International Journal of Advanced Networking and Applications 16, no. 05 (2025): 6591–95. https://doi.org/10.35444/ijana.2025.16508.

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By uncovering hidden patterns in big clinical datasets, deep learning has great promise for the medical industry in terms of aiding in the diagnosis of a wide range of diseases. A deterioration in brain function is a hallmark of Parkinson's disease (PD), a neurodegenerative condition. Early automated detection of Parkinson's disease is challenging due to the behavioral similarities between those with the disease and healthy individuals. Our objective is to offer a practical model that can facilitate the early detection of Parkinson's disease. We utilized the VGRF gait signal dataset, which was acquired via Physionet, to distinguish between individuals with Parkinson's disease and healthy individuals. A novel deep learning architecture based on LSTM networks is presented in this study to automatically detect freezing of gait episodes in Parkinson's disease. Unlike typical machine learning techniques, this method effectively captures long-term temporal correlations in gait patterns and eliminates the requirement for human feature engineering, improving the diagnosis of Parkinson's disease. To avoid the issue of vanishing gradients and enable optimal information absorption, the LSTM network uses memory blocks instead of selfconnected hidden units. Methods such as L2 regularization and dropout have been employed to prevent overfitting. Adam, an optimizer based on stochastic gradients, is also used in the optimization process. The results demonstrate that our proposed approach, with 97.71% accuracy, 99% sensitivity, 98% precision, and 96% specificity, surpasses the state-of-the-art models in FOG episode recognition. This demonstrates how promising it is as an improved classification method for Parkinson's disease diagnosis.

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Singh, Abhishek, Zohaib Hasan, Vishal Paranjape, Vedant Vashishtha, and Shubhanshika Chhabra. "Application of Machine Learning in Early Detection of Parkinson's disease Using Vocal Features." International Journal of Innovative Research in Science,Engineering and Technology 10, no. 11 (November 25, 2023): 14454–60. http://dx.doi.org/10.15680/ijirset.2021.1011128.

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Parkinson's disease (PD) is a neurodegenerative disorder that significantly impacts motor functions. Early and accurate detection of PD is crucial for effective treatment and management. This research explores the application of machine learning techniques to detect Parkinson's disease using vocal features from the UCI Parkinson's Disease Data Set. The study compares the performance of four machine learning models: Logistic Regression, Random Forest Classifier, Decision Tree Classifier, and Support Vector Machine (SVM). The dataset was split into training and testing sets, with each model evaluated based on accuracy and confusion matrix metrics. The Decision Tree Classifier and Random Forest Classifier achieved perfect accuracy on the training set, while the SVM model demonstrated the highest accuracy (89.74%) on the test set with a recall rate of 96.77%. These findings indicate that machine learning models, particularly SVM, can effectively contribute to the early detection of Parkinson's disease.

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Parbate, Pranay, Dhairyashil Thombare, Tushar Yerkal, Adarsh Varpe, and Prof Suresh Reddy. "Multiple Disease Detection System." International Journal for Research in Applied Science and Engineering Technology 12, no. 4 (April 30, 2024): 997–1003. http://dx.doi.org/10.22214/ijraset.2024.59966.

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Abstract: In the realm of healthcare, the early detection of multiple diseases presents a formidable challenge but holds immense potential for improving patient outcomes. This paper proposes an integrative approach for the simultaneous detection of four prevalent diseases: heart disease, Parkinson's disease, diabetes, and skin cancer. Leveraging advanced machine learning techniques, our framework encompasses data preprocessing methods for cleaning and normalization, feature selection strategies to extract discriminative features from heterogeneous medical datasets, and ensemble classification models for accurate disease prediction. Real-world datasets encompassing diverse medical conditions are utilized to evaluate the efficacy of the proposed framework. Experimental results demonstrate its superior performance in terms of accuracy, sensitivity, and specificity compared to conventional methods. This framework stands as a testament to the transformative potential of technology in healthcare, facilitating early detection and intervention across multiple diseases, thereby enhancing patient care and quality of life.

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Hui, Chen, Tee Connie, Michael Kah Ong Goh, and Nor ‘Izzati binti Saedon. "A Non-Invasive Gait-Based Screening Approach for Parkinson’s Disease." International Journal on Advanced Science, Engineering and Information Technology 14, no. 5 (October 25, 2024): 1639–48. http://dx.doi.org/10.18517/ijaseit.14.5.17461.

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Parkinson's disease (PD) presents a significant global health challenge, characterized by the progressive degeneration of dopamine-producing neurons in the brain, resulting in both motor and non-motor symptoms that severely impact quality of life. This study addresses the complexities of PD, highlighting the critical need for early diagnosis to slow disease progression. This research addresses the challenges of early diagnosis, such as the use of unreliable diagnostic techniques and limited healthcare resources. It uses the MMU Parkinson Disease Dataset and applies camera-based data collection to analyze gait patterns that can identify a risk of Parkinson's Disease. The study utilizes computer vision and the AlphaPose framework to analyze video data and detect body key points. By employing machine learning algorithms, including Support Vector Machines (SVM) and CatBoost, showing highly effective in identifying temporal dependencies in gait patterns. The algorithms achieved a high accuracy of 83.33% on the MMU dataset. This method enhances the accuracy of PD detection and enables immediate detection and control of the disease. The combination of advanced data analysis methods and medical knowledge offers new possibilities to develop targeted treatments that improve patient outcomes, demonstrating the potential of machine learning in effectively managing and treating Parkinson's disease. To enhance the generalizability of models, future research should collect extensive and diverse datasets covering various backgrounds and different stages of Parkinson's disease and utilize advanced techniques for extracting features to improve the accuracy of gait analysis.

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Abdullah Mohammed Almanaa. "Advancements In MRI Techniques For Early Detection Of Neurological Disorders." Journal of Namibian Studies : History Politics Culture 25 (January 10, 2019): 1621–37. http://dx.doi.org/10.59670/2e4rgt04.

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Magnetic Resonance Imaging (MRI) has become a cornerstone in the diagnosis and management of neurological disorders. Recent advancements in MRI technology have enhanced its capability to detect early signs of neurological diseases, providing opportunities for timely intervention and improved patient outcomes. This paper reviews the latest developments in MRI techniques and their applications in the early detection of neurological disorders such as Alzheimer's disease, multiple sclerosis, and Parkinson's disease.

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Vibhute, Anjali, Yogita Veer, Sai Venikar, and Ketaki Rathod. "Parkinson Disease Detection by Analyzing Spiral and Wave Drawings." International Journal for Research in Applied Science and Engineering Technology 11, no. 10 (October 31, 2023): 16–20. http://dx.doi.org/10.22214/ijraset.2023.56424.

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Abstract: Parkinson's disease is a ubiquitous, life altering neurological disorder that affects countless people and is often undiagnosed until its advanced stages. This delay in diagnosis means there is no time for timely intervention and better disease management. Our implementation process is set to change this narrative through the use of multimedia data processing techniques and technologies. Our system begins its mission by capturing and analyzing spiral images, which is a new method for diagnosing Parkinson's disease. It uses advanced image recognition technology to interpret specific patterns in these images, providing insight into possible early stages of the disease. Focused on the analysis of spiral images, our solutions aim to revolutionize the diagnostic process, providing people and doctors with powerful tools to diagnose Parkinson's disease earlier and more easily. This change has the potential to improve quality of life and reduce the burden of Parkinson's disease.

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Mr.Praveen and Dr Yasawini Vanapalli. "Deep Convolutional Neural Networks for Parkinson’sDisease Detection." Journal of Engineering Sciences 15, no. 11 (2024): 511–17. https://doi.org/10.36893/jes.2024.v15i11.055.

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Movement disorders are a hallmark of Parkinson's disease (PD) and are caused by the death of neurons in the brain that produce dopamine. Degeneration of nerve cells is a hallmark of Parkinson's disease. Symptoms of Parkinson's disease include tremors, rigidity, slow movements, tremors, and inability to maintain balance. In this study, we developed two neural network-based models, a VGFR spectrogram detector and a speech disorder classifier, to help both medical professionals and the general public in early diagnosis of the disease. To predict the disease, we used a deep and dense artificial neural network (ANN) on the voice recordings and a convolutional neural network (CNN) on the large amount of images of gait signals converted into spectrogram images. Experimental results show that the proposed model outperforms state-of-the-art methods

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Reddy, Aananya, Ruhananhad P. Reddy, Aryan Kia Roghani, Ricardo Isaiah Garcia, Sachi Khemka, Vasanthkumar Pattoor, Michael Jacob, P. Hemachandra Reddy, and Ujala Sehar. "Artificial intelligence in Parkinson's disease: Early detection and diagnostic advancements." Ageing Research Reviews 99 (August 2024): 102410. http://dx.doi.org/10.1016/j.arr.2024.102410.

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Yu, Zhenwei, Tessandra Stewart, Jan Aasly, Min Shi, and Jing Zhang. "Combining clinical and biofluid markers for early Parkinson's disease detection." Annals of Clinical and Translational Neurology 5, no. 1 (December 20, 2017): 109–14. http://dx.doi.org/10.1002/acn3.509.

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Archana Panda, Et al. "Early Detection of Parkinson's disease using a Machine Learning-Based Framework for Differentiating the Disease's with Various Stages." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 9 (November 5, 2023): 3368–74. http://dx.doi.org/10.17762/ijritcc.v11i9.9543.

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The term "Parkinson's disease" (PD) is mostly brought on by a disturbance of a brain's dopamine-producing cells, a chemical which permits communication between brain cells. Brain cells that produce dopamine control movement, flexibility, and fluency. After 60% to 80% of these cells are gone, dopamine production is inhibited, and Parkinson's disease symptoms start to emerge. Researchers are concentrating their efforts on finding any early non-movement symptoms to stop the disease's development because it is thought that the disease starts many years before any evident movement-related indicators occur. Early correct diagnosis of the condition is crucial to halt the continuous advancement of Parkinson's disease and give people access to medications that can slow the disease. To do this, ongoing research into the premotor stage of Parkinson's disease is necessary.

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BHANU PRAKASH, Kolla, and Valentina EMILIA BALAS. "DETECTING PARKINSON'S DISEASE USING A STACKED LONG SHORT-TERM MEMORY DEEP NEURAL NETWORK WITH FEATURE FUSION." Annals of the Academy of Romanian Scientists Series on Science and Technology of Information 16, no. 1-2 (2023): 46–68. http://dx.doi.org/10.56082/annalsarsciinfo.2023.1-2.46.

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Parkinson's disease is a neurodegenerative disorder that affects millions of people worldwide. Early disease detection is crucial for effective treatment, but diagnosis can be challenging due to the subtle symptoms. This paper proposes a novel approach for Parkinson's disease detection using the SeaLion Method for feature extraction and the SL Deep NN model for classification. The SeaLion Method is used to extract features from time series data collected from Parkinson's disease patients and healthy individuals, and these features are used to train the SL Deep NN model. The model's performance is evaluated using accuracy, precision, recall, and F1 score metrics. Our results demonstrate that the SL Deep NN model can accurately classify time series data as belonging to a Parkinson's patient or a healthy individual. We use 10-fold cross-validation to evaluate the performance of each model and compare the results using metrics such as accuracy, precision, recall, and F1 score. Our results demonstrate that all four models achieve high accuracy, with the SVM model performing the best with an accuracy of over 95%. Our approach shows promise for developing a non-invasive, accurate, and automated method for Parkinson's disease detection, which could improve early diagnosis and treatment of the disease.

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Faizah, Safira, Dian Nugraha, M. N. Mohammed, and Muhammad Irsyad Abdullah. "Design and Development Early Detection of Neurodegenerative Disease Using IoT Technology." Jurnal Informatika Universitas Pamulang 8, no. 2 (June 30, 2023): 292–97. http://dx.doi.org/10.32493/informatika.v8i2.32842.

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Parkinson's disease (PD) stands as one of the most prevalent conditions, impacting approximately 6.3 million individuals globally. This disorder becomes even more complex due to commonly associated non-motor symptoms like depression, cognitive impairments, and disruptions in sleep patterns. The root of PD remains largely unclear as a significant portion of cases lack a specific cause. In the initial phases of the illness, prominent indicators encompass tremors, rigidity, slowed motion, and difficulties in mobility. Presently, patients are obligated to have appointments with their medical practitioner at intervals of six months to a year, typically for brief consultations. The visit to the medical facility offers a limited glimpse into the patient's state, frequently failing to capture the day-to-day obstacles they encounter. The current evaluation methods are insufficient in comprehending this matter. This highlights the significance of promptly identifying PD, as it allows for the early implementation of treatment measures and management tactics. Additionally, this suggested approach contributes to the enhancement of human life within the healthcare framework and holds the potential to identify Parkinsonâ€™s disease swiftly and precisely.

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Miyamoto, Tomoyuki, Masayuki Miyamoto, Masaoki Iwanami, and Koichi Hirata. "Idiopathic REM Sleep Behavior Disorder: Implications for the Pathogenesis of Lewy Body Diseases." Parkinson's Disease 2011 (2011): 1–8. http://dx.doi.org/10.4061/2011/941268.

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Objectives. Both results of the odor identification and cardiac123I-metaiodobenzylguanidine accumulation have been investigated for their potential to enhance the detection of pathogenesis resembling that of Lewy body-relatedα-synucleinopathies in patients clinically diagnosed as having idiopathic REM sleep behavior disorder.Methods. We performed both the Odor Stick Identification Test for Japanese and123I-metaiodobenzylguanidine scintigraphy in 30 patients with idiopathic REM sleep behavior disorder, 38 patients with Parkinson's disease, and 20 control subjects.Results. In idiopathic REM sleep behavior disorder, reduced odor identification score and an early or delayed heart to mediastinum ratio on123I-metaiodobenzylguanidine were almost as severe as in Parkinson's disease patients. Delayed cardiac123I-metaiodobenzylguanidine uptake was even more severe in the idiopathic REM sleep behavior disorder group than in the Parkinson's disease group.Conclusions. Reduced cardiac123I-metaiodobenzylguanidine uptake, which is independent of parkinsonism, may be more closely associated with idiopathic REM sleep behavior disorder than olfactory impairment.

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36

Zhang, Yitao, Han Yu, Chenyang Sun, and Mingheng Jin. "Parkinsons disease detection based on image analysis of EEG signals." Theoretical and Natural Science 43, no. 1 (July 26, 2024): 246–52. http://dx.doi.org/10.54254/2753-8818/43/20240980.

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Parkinsons disease (PD) is a common chronic neurological disease, that causes great disturbance to the patient's life and work, and when the disease develops seriously, it may even lead to the death of the patient. Until now, treating PD has been a tough nut to crack and a financial challenge for families and governments alike. In this paper, we propose to use the Resnet-50 Neural Network model to differentiate between 41 PD patients and 41 normal subjects by analyzing time-frequency domain maps of electroencephalography (EEG) signals. This method achieves classification accuracies ranging from 81% to 85% for six-channel detection and varying from 76% to 77% for single-channel detection, which opens up new avenues for the early diagnosis of Parkinson's disease, demonstrating the potential to combine EEG signals with image processing.

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Krishna, A. Yuva, K. Ravi Kiran, N. Raghavendra Sai, Aditi Sharma, S. Phani Praveen, and Jitendra Pandey. "Ant Colony Optimized XGBoost for Early Diabetes Detection: A Hybrid Approach in Machine Learning." Journal of Intelligent Systems and Internet of Things 10, no. 2 (2023): 76–89. http://dx.doi.org/10.54216/jisiot.100207.

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The primary objective of this research endeavour is to concentrate on the timely detection and prognostication of diabetes and Parkinson's disease through the utilisation of machine learning techniques, specifically the integration of Ant Colony Optimisation (ACO) with the XGBoost algorithm (ACXG). The healthcare issues presented by diabetes and Parkinson's disease underscore the criticality of early detection in order to facilitate effective intervention and enhance patient outcomes. The objective of this work is to establish a connection between the prediction of diabetes and the classification of Parkinson's disease, thereby developing a comprehensive model that improves the prognosis and prevention of these diseases. The project entails the collection and pre-processing of pertinent datasets, afterwards employing a range of classification approaches such as Logistic Regression, Support Vector Machine (SVM), Random Forest, and the innovative ACO-XGBoost model. The results of performance comparisons demonstrate that ACO-XGBoost has superior performance in contrast to conventional approaches. It achieves notable levels of accuracy, precision, recall, F1-score, and AUC, hence establishing its significance as a valuable tool for disease prediction. The incorporation of Ant Colony Optimisation (ACO) with XGBoost (ACXG) showcases the capacity to augment predictive precision and sensitivity, presenting notable progressions in healthcare methodologies. The present study makes a valuable contribution to the advancement of more accurate predictive models, ultimately enhancing the quality of patient care and public health outcomes.

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38

Navada, Megha, Deepshikha Mishra, Saloni Parkar, Parag Patil, and Chaitanya Jage. "Early Stage Detection of Parkinson Disease." ITM Web of Conferences 40 (2021): 03050. http://dx.doi.org/10.1051/itmconf/20214003050.

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Parkinson’s disease is a chronic neurodegenerative condition that demonstrate the progressive loss of the ability to correlate movements mainly occurs in the elderly. For the purpose of monitoring tremors in Parkinson’s disease, a system has to be designed and developed. For coordination of movements, people with Parkinson’s, deprive of a chemical called dopamine which behaves as the messenger between the brain parts and the nervous system .Detecting Parkinson’s disease is a very arduous task as there is no evidence currently present to do this. Therefore, the main intention of our work is the designing of a system for recognizing Parkinson’s disease at an initial stage. An Android application is being designed that allows the status of PD patients to be assessed based on the tests found on the Unified Parkinson’s Disease Rating Scale approved by the Movement Disorders Society (MDS-UPDRS).

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39

Md Abu Sayed, Maliha Tayaba, MD Tanvir Islam, Md Eyasin Ul Islam Pavel, Md Tuhin Mia, Eftekhar Hossain Ayon, Nur Nob, and Bishnu Padh Ghosh. "Parkinson's Disease Detection through Vocal Biomarkers and Advanced Machine Learning Algorithms." Journal of Computer Science and Technology Studies 5, no. 4 (December 2, 2023): 142–49. http://dx.doi.org/10.32996/jcsts.2023.5.4.14.

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Parkinson's disease (PD) is a prevalent neurodegenerative disorder known for its impact on motor neurons, causing symptoms like tremors, stiffness, and gait difficulties. This study explores the potential of vocal feature alterations in PD patients as a means of early disease prediction. This research aims to predict the onset of Parkinson's disease. Utilizing a variety of advanced machine-learning algorithms, including XGBoost, LightGBM, Bagging, AdaBoost, and Support Vector Machine, among others, the study evaluates the predictive performance of these models using metrics such as accuracy, area under the curve (AUC), sensitivity, and specificity. The findings of this comprehensive analysis highlight LightGBM as the most effective model, achieving an impressive accuracy rate of 96% alongside a matching AUC of 96%. LightGBM exhibited a remarkable sensitivity of 100% and specificity of 94.43%, surpassing other machine learning algorithms in accuracy and AUC scores. Given the complexities of Parkinson's disease and its challenges in early diagnosis, this study underscores the significance of leveraging vocal biomarkers coupled with advanced machine-learning techniques for precise and timely PD detection.

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Goel, Manav, Ananda Kumar S, P. Jyotheeswari, Sangeetha R, and Sarojini Balakrishnan. "Early Detection of Parkinson Disease using Voice Data." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 11s (October 7, 2023): 580–85. http://dx.doi.org/10.17762/ijritcc.v11i11s.8188.

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Parkinson’s disease affects over 10 million people worldwide, with approximately 20 percent of patients not being diagnosed. Clinical diagnosis is expensive because there are no specific tests or bio-markers, and it can take days to diagnose because it is based on a comprehensive evaluation of the individual’s symptoms. Existing research either predicts a Unified Parkinson Disease Rating Scale rating, uses other key Parkinsonian features to diagnose an individual, such as tapping, gait, and tremor, or focuses on different audio features. In this paper, we are focusing on using the voice aspect for the early detection of the disease. We use the University of California Irvine (UCI) Parkinson data set. This data set contains various parameters regarding voice jitter. The data set first undergoes preprocessing. We have used a Feedforward Neural Network (FNN) model to acquire early on detection using the above data set. Our model has achieved an efficiency of 97.43 percent. This efficiency can be improved by using even a larger and diverse data set.

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41

Ho, Nguyen H. B., Dalton Lee Glasco, Rhys N. Sopp, and Jeffrey Gordon Bell. "Multiplexed Electrochemical Device for the Detection of Biomarkers of Parkinson’s Disease Using 3D Printing." ECS Meeting Abstracts MA2022-02, no. 61 (October 9, 2022): 2231. http://dx.doi.org/10.1149/ma2022-02612231mtgabs.

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Neurotransmitters are chemical messengers that carry signals between cells. Imbalances in neurotransmitter levels have been linked to physical, psychotic, and neurodegenerative diseases such as Alzheimer's, Parkinson's, dementia, addiction, depression, and schizophrenia. Therefore, the rapid detection of neurotransmitters such as dopamine, acetylcholine, glutamate, serotine, and norepinephrine are essential for early diagnosis of various neurological disorders. This talk will focus on the development of a multiplexed electrochemical device using 3D printing for the simultaneous detection of biomarkers related to Parkinson’s disease. By combining the capabilities of stereolithographic (SLA) and fused-deposition modelling (FDM) 3D printing, we demonstrate the ability to fabricate and integrate potentiometric and voltametric sensors into the device. Importantly, we show that this approach is highly modifiable and scalable, providing a pathway towards the development of a full-panel diagnostic test for neurological disorders.

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Shaikh, Mr Zaki Ahmed, Mr Viraj Tilekar, Mr Vedant Suryawanshi, Mr Atharva Pawar, and Mr Nitin R. Talhar. "Parkinson Disease Detection from Spiral and Wave Drawings using Machine Learning Algorithm." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (May 31, 2023): 7523–27. http://dx.doi.org/10.22214/ijraset.2023.53503.

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Abstract: Research in biometrics has experienced significant growth in recent years, leading to a wide range of applications. One prominent area where biometrics has proven invaluable is healthcare. In the field of healthcare, identifying relevant biomarkers specific to certain fitness issues and detecting them accurately is crucial for improving medical decision assistance systems. Parkinson's Disease (PD) is a condition where impairment in handwriting has been observed to correlate directly with disease severity. Additionally, individuals with Parkinson's disease tend to exhibit lower velocity and pressure while using a pen for sketching or writing. Detecting such biomarkers accurately and precisely at the early stages of the disease can greatly enhance medical diagnosis. Consequently, a system has been designed to analyse spiral and wave drawing patterns of individuals affected by Parkinson's disease. By leveraging various machine learning algorithms, it becomes possible to analyse these patterns and determine whether a person is suffering from Parkinson's disease or not.

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Sadiq, Mohd, Mohd Tauheed Khan, and Sarfaraz Masood. "Attention-Based Deep Learning Model for Early Detection of Parkinson's Disease." Computers, Materials & Continua 71, no. 3 (2022): 5183–200. http://dx.doi.org/10.32604/cmc.2022.020531.

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Bonsu, Enock Adu. "Leveraging Ml for Early Detection and Management of Diabetes and Parkinson's Disease: Innovations in Predictive Analytics and Personalized Healthcare." International Journal of Research Publication and Reviews 5, no. 11 (November 2024): 761–81. http://dx.doi.org/10.55248/gengpi.5.1124.3133.

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45

Prasad, Rajeshwar, and Amit Kumar Saxena. "A Machine Learning Approach for an Early Prediction of Parkinson’s disease." Indian Journal Of Science And Technology 17, no. 33 (August 24, 2024): 3410–18. http://dx.doi.org/10.17485/ijst/v17i33.2091.

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Objectives: Parkinson's disease (PD) is a critical disease and the early detection of PD is of high importance to start a proper treatment to avoid its emergence. An Optimized Filter-based Machine Learning Classifier (OFMLC) approach is used in this paper for prediction of PD. Method: The PD data is first balanced using random over-sampling, random down-sampling, and the Synthetic Minority Over-sampling Technique (SMOTE). Then, the SelectKBest approach is used to select K filter-based features. The dataset collected from the University of Oxford library has 195 items, 24 attributes (features), and two classes viz. healthy individuals and individuals with PD symptoms. The extensive experiments were performed with the 10 and 18 most relevant features of the dataset. The results are compared with some of the existing methods namely WFSK-NN, RFEANN, RFESVM, and ECL. The performance of the model is evaluated using the metrics: accuracy, precision, F1 scores, and recall. Findings: The proposed approach with 10 most important features of the PD dataset produced a maximum accuracy of 99.98% using Random Forest (RF), Gradient Boost (GB), Voting, Stacking, XG Boost (XG), LightGBM (LG) and CatBoost (CB) classifiers; while with 18 features, the Random Forest RF, Bagging, Stacking, LightGBM (LG) and CatBoost (CB) produced 99.98 % accuracy. Novelty: The proposed method applied firstly the data balancing method to balance the data for maintaining a proper class ratio among the patterns of the PD dataset to ensure unbiased data selection for experiments. The SelectKBest method has the advantage of improving the model's performance, making computations easier, and resulting in higher accuracy over other methods. The OFMLC approach is applied to the reduced feature subset which improves the accuracy of the model with different classifiers which justifies the strength of the proposed method. Keywords: Machine Learning, Filter-based Feature Selection, Data Balancing, SelectKBest, Grid Search, Ensemble Classifiers

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Magare, Archana Chandrashekhar, and Maulika Patel. "Parkinson’s Disease Interstage Prognostic Biomarkers for Early Detection through Hybrid Machine Learning Model." Indian Journal Of Science And Technology 17, no. 40 (October 31, 2024): 4209–20. http://dx.doi.org/10.17485/ijst/v17i40.2254.

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Background: Parkinson’s Disease (PD) is a neurodegenerative condition that takes 15 to 20 years to exhibit the symptoms. However, during the initial stages of the disease, there are microlevel changes in the body that are typically undetected. The pathogenesis of Parkinson’s disease requires understanding of these microlevel changes. Objectives: The objective of the present study is to develop a hybrid ensembled machine learning pipeline model for identifying inter-stage Parkinson’s Disease (PD) biomarkers for understanding disease progression as well as etiology. Methods: The proposed work was carried out on the dataset GSE202667 from Gene Expression Omnibus, containing time-resolved RNA signatures of CD4+ T cells at various stages. Differentiating genes were identified in different interstage groups. Two types of unsupervised learning methods- distance-based (K-means, Agglomerative, Density-Based Spatial Clustering of Application with Noise) and probability-based (Hidden Markov Model, Gaussian Mixture Model Latent Dirichlet Allocation) were applied. The best algorithms were selected and applied to optimize clusters. Enrichment analysis was conducted on the top 10 PD biomarkers in each category. Findings: The top 10 PD biomarkers in each category are identified and gene set enrichment analysis resulted into their enrichment in three KEGG Pathways and depletion in two GO molecular functions. These biomarkers’ depletion is observed in 215 Reactome pathways and enrichment in 18 Reactome pathways. Twelve GO-Cellular components had an enrichment whereas 111 GO-Cellular components had a depletion in the gene set. A total of 25 GO-Biological process components were enriched, while 339 GO-Biological process components were depleted. Novelty: The proposed hybrid ensembled machine learning pipeline model works as a tool to identify Parkinson’s Disease biomarkers from omics data. The model contributes to identify implicit patterns in omics data in order to unveil Parkinson’s disease progress mechanism through biomarkers discovery. Keywords: Gaussian Mixture Model clustering, Early Detection, k-means clustering, Machine learning, Parkinson’s Disease Interstage Biomarkers

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47

Zapata-Paulini, Joselyn, and Michael Cabanillas-Carbonell. "Evaluation of machine learning algorithms in the early detection of Parkinson's disease: a comparative study." Indonesian Journal of Electrical Engineering and Computer Science 35, no. 1 (July 1, 2024): 222. http://dx.doi.org/10.11591/ijeecs.v35.i1.pp222-237.

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Parkinson's is a neurodegenerative disease that generally affects people over 60 years of age. The disease destroys neurons and increases the accumulation of α-synuclein in many parts of the brain stem, although at present its causes remain unknown. It is therefore a priority to identify a method that can detect the disease, and this is where machine learning models become important. This study aims to perform a comparative analysis of machine learning models focused on the early detection of Parkinson's disease. Logistic regression (LR), support vector machines (SVM), decision trees (DT), extra trees classifiers (ETC), K-nearest neighbors (KNN), random forests (RF), adaptive boosting (AdaBoost) and gradient boosting (GB) algorithms are described and developed to identify the one that offers the best performance. In the training stage, we used the Oxford University dataset for Parkinson's disease detection, which has a total of 23 attributes and 195 records on patient voice recordings. The article is structured into six sections, such as introduction, related work, methodology, results, discussions, and conclusions. The metrics of accuracy, sensitivity, F1 count, and precision were used to measure the models' performance. The results position the KNN model as the best predictor with 95% accuracy, precision, sensitivity, and F1 score.

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Bai, Ms P. Annapurna, Yeruva Bala Tejaswini, Vana Kameswara Rao, and Ponnapalli Omkareaswari Bhavana. "Parkinson Disease Detection Using CNN Algorithm." International Journal for Research in Applied Science and Engineering Technology 12, no. 5 (May 31, 2024): 2342–46. http://dx.doi.org/10.22214/ijraset.2024.62084.

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Abstract: Parkinson's disease, a progressive neurological disorder, results from the depletion of dopamine-producing neurons in the brain, leading to diminished motor function. Common symptoms include tremors, rigidity, bradykinesia, shivering, and impaired balance. This study introduces two neural network architectures: the Voice Impairment Classifier, designed for early disease detection. The research conducted a thorough assessment of convolutional neural networks (CNNs) for classifying gait signals transformed into spectrogram images, and deep dense networks for analyzing voice recordings. Results demonstrated superior performance of the proposed models, with the VGFR Spectrogram Detector achieving 88.1% accuracy and the Voice Impairment Classifier achieving 89.15% accuracy, surpassing current state-of-the-art techniques.

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Hani, Ahmed Alaa, Amira Bibo Sallow, Hawar Bahzad Ahmad, Saman Mohammed Abdulrahman, Renas Rajab Asaad, Subhi R. M. Zeebaree, and Dilovan Asaad Majeed. "COMPARATIVE ANALYSIS OF STATE-OF-THE-ART CLASSIFIERS FOR PARKINSON'S DISEASE DIAGNOSIS." Jurnal Ilmiah Ilmu Terapan Universitas Jambi 8, no. 2 (September 23, 2024): 409–23. http://dx.doi.org/10.22437/jiituj.v8i2.32771.

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Parkinson's disease (PD) presents a growing global health challenge, with early detection being crucial for effective management and treatment. This study seeks to develop an innovative machine learning (ML) framework for the early detection of PD by integrating advanced techniques for data preprocessing, dimensionality reduction, feature selection, and ensemble classification, aiming to significantly improve detection accuracy and timeliness. The research employs a robust ML pipeline, beginning with data preprocessing using mean imputation, standardization, min-max scaling, and SMOTE (Synthetic Minority Over-sampling Technique) to handle imbalanced data. Dimensionality reduction is achieved through Principal Component Analysis (PCA), while feature selection is performed using SelectKBest coupled with the ANOVA F-test to identify the most relevant features. Four ensemble methods—Random Forest, Gradient Boosting, XGBoost, and Support Vector Machine (SVM)—are evaluated for classification. Among the classifiers tested, the Gradient Boosting model stands out with an impressive accuracy of 0.9487, demonstrating its superior performance in PD detection. Integrating multiple preprocessing, dimensionality reduction, and feature selection techniques proves essential in optimizing model performance, highlighting the importance of a multifaceted approach in handling complex datasets. This research introduces a comprehensive ML framework that combines multiple advanced techniques in a streamlined process, significantly improving the early detection of Parkinson's disease. Ensemble methods, combined with strategic feature selection and data balancing techniques, offer a novel approach that could be applied to other neurodegenerative disorders, expanding its potential impact beyond PD detection.

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Ranjan, Nihar M., Gitanjali Mate, and Maya Bembde. "Detection of Parkinson's Disease using Machine Learning Algorithms and Handwriting Analysis." Journal of Data Mining and Management 8, no. 1 (March 29, 2023): 21–29. http://dx.doi.org/10.46610/jodmm.2023.v08i01.004.

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Parkinson's Disease is a progressive neurodegenerative disorder of movement that affects your ability to control movement. This disease can prove fatal if not detected at an earlier stage. Motor and non-motor symptoms are raised by the loss of dopamine-producing neurons. Currently, there is no test available to detect disease at early stages where the symptoms may be poorly characterised. Handwriting analysis is one of the traditional aspects of studying human personality and also can be used to identify the symptoms of this disease. Identifying such accurate biomarkers provides roots for better clinical diagnosis. In this paper, we proposed a system that makes use of two types of handwriting analysis, spiral and wave drawings of healthy as well as Parkinson's patients as an input to the system. For feature extraction, we are using a histogram of the oriented gradient. The developed system uses a machine learning algorithm and a random forest classifier for the detection of Parkinson's disease among patients. Our model achieved an accuracy of 86.67 % in the case of spiral drawing and 83.30% with wave drawing.

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