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Artículos de revistas sobre el tema "Medical Prediction"

Autor: Grafiati

Publicado: 29 de marzo de 2025

Última modificación: 31 de julio de 2025

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1

Sabarinath U S and Ashly Mathew. "Medical Insurance Cost Prediction." Indian Journal of Data Communication and Networking 4, no. 4 (2024): 1–4. http://dx.doi.org/10.54105/ijdcn.d5037.04040624.

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This is a medical insurance cost prediction model that uses a linear regression algorithm to predict the medical insurance charges of a person based on the given data. To predict things that have never been so easy. In this project used to predict values that wonder how Insurance amount is normally charged. This is a medical insurance cost prediction model that uses a linear regression algorithm to predict the medical insurance charges of a person based on the given data. This project on predicting medical insurance costs can serve various purposes and address several needs that are Accurate P

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Sabarinath, U. S. "Medical Insurance Cost Prediction." Indian Journal of Data Communication and Networking (IJDCN) 4, no. 4 (2024): 1–4. https://doi.org/10.54105/ijdcn.D5037.04040624.

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<strong>Abstract:</strong> This is a medical insurance cost prediction model that uses a linear regression algorithm to predict the medical insurance charges of a person based on the given data. To predict things that have never been so easy. In this project used to predict values that wonder how Insurance amount is normally charged. This is a medical insurance cost prediction model that uses a linear regression algorithm to predict the medical insurance charges of a person based on the given data. This project on predicting medical insurance costs can serve various purposes and address severa

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3

K. Tanusha, M. Varsha, E. Naveen Kumar, K. Pavan Kalayan, and P.M. Suresh. "Medical Insurance Price Prediction Using ML." International Research Journal on Advanced Engineering and Management (IRJAEM) 3, no. 05 (2025): 2111–16. https://doi.org/10.47392/irjaem.2025.0333.

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The rising significance of health insurance in the aftermath of the COVID-19 pandemic has spurred numerous initiatives aimed at better understanding and managing medical insurance costs. This study presents a machine learning-based approach for predicting health insurance expenses using a dataset sourced from Kaggle. The primary objective is to develop a predictive system that assists individuals in making cost-effective insurance decisions and supports policymakers in identifying and regulating high-cost providers. Various regression algorithms were explored to capture the complex relationshi

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4

Liu, Enwu, Ryan Yan Liu, and Karen Lim. "Using the Weibull Accelerated Failure Time Regression Model to Predict Time to Health Events." Applied Sciences 13, no. 24 (2023): 13041. http://dx.doi.org/10.3390/app132413041.

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Clinical prediction models are commonly utilized in clinical practice to screen high-risk patients. This enables healthcare professionals to initiate interventions aimed at delaying or preventing adverse medical events. Nevertheless, the majority of these models focus on calculating probabilities or risk scores for medical events. This information can pose challenges for patients to comprehend, potentially causing delays in their treatment decision-making process. Our paper presents a statistical methodology and protocol for the utilization of a Weibull accelerated failure time (AFT) model in

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Ramesh, Banoth, G. Srinivas, P. Ram Praneeth Reddy, M. D. Huraib Rasool, Divya Rawat, and Madhulita Sundaray. "Feasible Prediction of Multiple Diseases using Machine Learning." E3S Web of Conferences 430 (2023): 01051. http://dx.doi.org/10.1051/e3sconf/202343001051.

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Automated Multiple Disease Prediction System using Machine Learning is an advanced healthcare application that utilizes machine learning algorithms to accurately predict the likelihood of a patient having multiple diseases based on their medical history and symptoms. The system employs a comprehensive dataset of medical records and symptoms of various diseases, which are then analysed using machine learning techniques such as decision trees, support vector machines, and random forests. The system’s predictions are highly accurate, and it can assist medical professionals in making more informed

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Kannan, S., G. Premalatha, M. Jamuna Rani, et al. "Effective Evaluation of Medical Images Using Artificial Intelligence Techniques." Computational Intelligence and Neuroscience 2022 (August 10, 2022): 1–9. http://dx.doi.org/10.1155/2022/8419308.

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This work is implemented for the management of patients with epilepsy, and methods based on electroencephalography (EEG) analysis have been proposed for the timely prediction of its occurrence. The proposed system is used for crisis detection and prediction system; it is useful for both patients and medical staff to know their status easily and more accurately. In the treatment of Parkinson’s disease, the affected patients with Parkinson’s disease can assess the prognostic risk factors, and the symptoms are evaluated to predict rapid progression in the early stages after diagnosis. The present

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P., Renukadevi. "Lossless Medical Image Compression by Multi Oriented Prediction Technique." International Journal of Psychosocial Rehabilitation 24, no. 5 (2020): 1277–96. http://dx.doi.org/10.37200/ijpr/v24i5/pr201800.

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Prof. M. S. Patil, Kulkarni Sanika, and Khurpe Sanjana. "MEDICAL INSURANCE PREMIUM PREDICTION WITH MACHINE LEARNING." International Journal of Innovations in Engineering Research and Technology 11, no. 5 (2024): 5–11. http://dx.doi.org/10.26662/ijiert.v11i5.pp5-11.

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A machine learning method for predicting health insurance rates is presented in this article. With healthcare expenditures becoming more complex, it is critical for insurance companies and policyholders to accurately estimate insurance prices. Utilizing a dataset that included medical history, demographic data, and other pertinent variables, a variety of machine learning techniques, such as ensemble methods and regression, were used to create prediction models. R-Squared and mean absolute error were two measures used to assess these models' performance. According to the developed models' resul

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Ben Shoham, Ofir, and Nadav Rappoport. "CPLLM: Clinical prediction with large language models." PLOS Digital Health 3, no. 12 (2024): e0000680. https://doi.org/10.1371/journal.pdig.0000680.

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We present Clinical Prediction with Large Language Models (CPLLM), a method that involves fine-tuning a pre-trained Large Language Model (LLM) for predicting clinical disease and readmission. We utilized quantization and fine-tuned the LLM using prompts. For diagnostic predictions, we predicted whether patients would be diagnosed with a target disease during their next visit or in the subsequent diagnosis, leveraging their historical medical records. We compared our results to various baselines, including Retain and Med-BERT, the latter of which is the current state-of-the-art model for diseas

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Takke, Kunal, Rameez Bhaijee, Avanish Singh, and Mr Abhay Patil. "Medical Disease Prediction using Machine Learning Algorithms." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (2022): 221–27. http://dx.doi.org/10.22214/ijraset.2022.42135.

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Abstract: There is a growing importance of healthcare and pandemic has proved that healthcare is an important aspect of an individual life. Most of the medical diagnoses require going to the doctor and fixing appointments for a consultation and sometimes to get accurate disease indications we have to wait for blood reports also we have to travel long distances to seek doctor consultation. When we are not feeling well the first thing we do is to check our temperature to get an estimate or baseline idea of our fever so we can consult our doctor if the temperature is high enough similarly a medic

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R, Ashwini, S. M. Aiesha Afshin, Kavya V, and Prof Deepthi Raj. "Diabetes Prediction Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (2022): 544–49. http://dx.doi.org/10.22214/ijraset.2022.41143.

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Abstract: The concept of machine learning has quickly become very attractive to the healthcare industry. Predictions and analyzes made by the research community on medical data sets help with appropriate care and precautions in the prevention of disease. of machine learning, the types of algorithms that can help make decisions and predictions. We also discuss various applications of machine learning in the medical field, with a focus on diabetes prediction through machine learning. Diabetes is one of the most increasing diseases in the world and it requires continuous monitoring. To check this

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Severn, Cameron, Krithika Suresh, Carsten Görg, Yoon Seong Choi, Rajan Jain, and Debashis Ghosh. "A Pipeline for the Implementation and Visualization of Explainable Machine Learning for Medical Imaging Using Radiomics Features." Sensors 22, no. 14 (2022): 5205. http://dx.doi.org/10.3390/s22145205.

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Machine learning (ML) models have been shown to predict the presence of clinical factors from medical imaging with remarkable accuracy. However, these complex models can be difficult to interpret and are often criticized as “black boxes”. Prediction models that provide no insight into how their predictions are obtained are difficult to trust for making important clinical decisions, such as medical diagnoses or treatment. Explainable machine learning (XML) methods, such as Shapley values, have made it possible to explain the behavior of ML algorithms and to identify which predictors contribute

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Reiz, Beáta, and Lehel Csató. "Bayesian Network Classifier for Medical Data Analysis." International Journal of Computers Communications & Control 4, no. 1 (2009): 65. http://dx.doi.org/10.15837/ijccc.2009.1.2414.

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<p>Bayesian networks encode causal relations between variables using probability and graph theory. They can be used both for prediction of an outcome and interpretation of predictions based on the encoded causal relations. In this paper we analyse a tree-like Bayesian network learning algorithm optimised for classification of data and we give solutions to the interpretation and analysis of predictions. The classification of logical – i.e. binary – data arises specifically in the field of medical diagnosis, where we have to predict the survival chance based on different types of medical o

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LAHARI, PEKETI, and G. MANOJ KUMAR. "Medical Insurance Cost Prediction Using Machine Learning." International Scientific Journal of Engineering and Management 04, no. 07 (2025): 1–9. https://doi.org/10.55041/isjem04871.

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The increasing cost of healthcare services has made medical insurance a crucial financial tool. Predicting insurance costs accurately helps insurance providers assess risk and allows customers to plan better. This project focuses on building a machine learning model to predict individual medical insurance charges using key features such as age, sex, BMI, number of children, smoking status, and region. The model uses linear regression as a base method to map the relationship between these variables and insurance cost. The data is pre-processed and visualized using exploratory data analysis (EDA

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15

Ahuja, Teesha. "Employability of the Machine Learning Algorithms in the Early Diagnosis of Various Diseases." International Journal of Research in Medical Sciences and Technology 13, no. 01 (2022): 158–63. http://dx.doi.org/10.37648/ijrmst.v13i01.015.

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The most difficult task is accurately predicting disease. Environment and lifestyle factors contribute to a wide range of illnesses. As a result, it becomes a crucial task to predict disease earlier. On the other hand, the doctor finds it too difficult to predict symptoms accurately. Predicting the disease is important in using data mining to solve this issue. Medical science experiences significant annual data growth. Early patient care has benefited from accurate medical data analysis because of the growing amount of data in the medical field. Data mining uncovers hidden pattern information

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Supriya, M., and A. J. Deepa. "A Survey on Prediction Using Big Data Analytics." International Journal of Big Data and Analytics in Healthcare 2, no. 1 (2017): 1–15. http://dx.doi.org/10.4018/ijbdah.2017010101.

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This article describes how nowadays, the growth of big data in bio-medical and healthcare community services is increasing rapidly. The early detection of diseases and patient care are analyzed with the help of accurate analysis of medical data includes diagnosed patients' details. The analysis of accuracy rate is considerably reduced when the quality of medical data is unclear since every part of the body has unique characteristics of certain regional diseases that may suppress the prediction of diseases. This article reviews the detailed survey of different prediction methods developed for a

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Azmi, Fadhillah, and Amir Saleh. "A Hybrid Algorithm for Multiple Disease Prediction: Radial Basis Function and Logistic Regression." International Journal of Science and Healthcare Research 9, no. 2 (2024): 363–68. http://dx.doi.org/10.52403/ijshr.20240246.

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Disease prediction is an important aspect of modern medicine, which aims to diagnose disease early and provide appropriate treatment to patients. This research uses a hybrid approach that combines the RBF (Radial Basis Function) kernel algorithm with logistic regression to predict various diseases in medical datasets. This method is intended to improve prediction performance by exploiting the advantages of each algorithm. This research uses a dataset containing medical information about several diseases collected from the Kaggle dataset. First, the RBF kernel is applied to transform the data f

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Prazdnikova, Margaryta. "Prediction and Assessment of Myocardial Infarction Risk on the Base of Medical Report Text Collection." Cybernetics and Computer Technologies, no. 4 (December 18, 2024): 71–80. https://doi.org/10.34229/2707-451x.24.4.7.

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Introduction. Myocardial infarction remains one of the leading causes of death worldwide, resulting from sudden disruption of blood supply to the heart muscle. Key risk factors include smoking, age, gender, high cholesterol levels, diabetes, and others. Despite advancements in diagnostics and treatment, early detection of heart attack risk is crucial for reducing mortality and improving patient quality of life. This paper explores an approach to predicting heart attack risk based on analysis of text data of medical reports using machine learning. The purpose of the article is to demonstrate ho

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Tian, Mohan, Yingci Li, and Hong Chen. "18F-FDG PET/CT Image Deep Learning Predicts Colon Cancer Survival." Contrast Media & Molecular Imaging 2023 (May 4, 2023): 1–10. http://dx.doi.org/10.1155/2023/2986379.

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Colon cancer is a type of cancer that begins in the large intestine. In the process of efficacy evaluation, postoperative recurrence prediction and metastasis monitoring of colon cancer, traditional medical image analysis methods are highly dependent on the personal ability of the doctors. In the process of patient treatment, it not only increases the workload and work pressure for doctors, but also has some problems with traditional medical image analysis methods. Moreover, the traditional medical image analysis methods have problems such as insufficient prediction accuracy, slow prediction s

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Perepeka, Eugene, Vasyl Lazoryshynets, Vitalii Babenko, Illia Davydovych, and Ievgen Nastenko. "Cardiomyopathy prediction in patients with permanent ventricular pacing using machine learning methods." System research and information technologies, no. 1 (March 29, 2024): 33–41. http://dx.doi.org/10.20535/srit.2308-8893.2024.1.03.

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Pacing-induced cardiomyopathy is a notable issue in patients needing permanent ventricular pacing. Identifying risk groups early and swiftly preventing the ailment can reduce patient harm. However, current prognostic methods require clarity. We employed machine learning to develop predictive models using medical data. Three algorithms — decision tree, group method of data handling, and logistic regression — formed models that forecast pacing-induced cardiomyopathy. These models displayed high accuracy in predicting development, signifying soundness. Factors like age, paced QRS width, pacing mo

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Liu, Chang Chun, Tao Wu, and Cheng He. "State of health prediction of medical lithium batteries based on multi-scale decomposition and deep learning." Advances in Mechanical Engineering 12, no. 5 (2020): 168781402092320. http://dx.doi.org/10.1177/1687814020923202.

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To guarantee rescue time and reduce medical accidents, a health degradation prediction model of medical lithium-ion batteries based on multi-scale deep neural network was proposed aiming at the problems of poor model adaptability and inaccurate prediction in current state of health prediction methods. The collected energy data of medical lithium-ion batteries were decomposed into main trend data and fluctuation data by ensemble empirical mode decomposition and correlation analysis. Then, deep Boltzmann machines and long short-term memory were used to model the main trend and fluctuation data,

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Mankar, Aayush, Atharv Pawar, Akash Pore, and Amar Waghmare. "Different Disease Prediction." International Journal for Research in Applied Science and Engineering Technology 11, no. 11 (2023): 1907–10. http://dx.doi.org/10.22214/ijraset.2023.56897.

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Abstract: One of the most significant subjects of society is human healthcare. It is looking for the best one and robust disease diagnosis to get the care they need as soon as possible.The task of following new approaches is challenging these disciplines, moving beyond the conventional ones.The actual number of new techniques makes it possible to provide a broad overview that avoids particular aspects. To this end, we suggest a systematic analysis of human diseases related to machine learning. This research concentrates on existing techniques related to machine learning growth applied to the d

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Alamelu, J. V., and Mythili Asaithambi. "EVALUATION OF MEDICAL GRADE INFUSION PUMP PARAMETER USING GAUSSIAN PROCESS REGRESSION." Biomedical Sciences Instrumentation 58, no. 2 (2022): 59–66. http://dx.doi.org/10.34107/nsjx733559.

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Prediction techniques are extensively used in medical applications and health care devices. The prediction of the infusion flow rate for the required drug dosage and drug concentration in a smart wireless infusion pump is necessary for precise drug flow for the patients. In this paper, the prediction model has been developed to predict the lag time using Gaussian Process Regression (GPR) technique with a squared exponential kernel. Currently, a smart wireless infusion pump is incorporated with its smart drug library. The required parameters such as drug dosage, drug flow rate are utilized as i

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Alamelu, J. V., and A. Mythili. "Evaluation of medical grade infusion pump parameters using Gaussian Process Regression." EAI Endorsed Transactions on Pervasive Health and Technology 8, no. 5 (2022): e3. http://dx.doi.org/10.4108/eetpht.v8i5.3171.

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Prediction techniques are extensively used in medical applications and health devices. The prediction of the infusion flow rate and its speed in a smart wireless infusion pump is necessary to provide precise drug flow. This paper has developed the prediction model to predict the lag time and infusion pump speed using the Gaussian process regression (GPR) technique with a squared exponential kernel. The present smart wireless infusion pump is usually incorporated with its smart drug library. The required parameters such as drug dosage, drug flow rate are utilized as inputs to predict the pump s

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Setyonugroho, Winny, Sentagi Sesotya, Iman Permana, Tri Lestari, Didit Mahendra, and Habib Abda. "Enhancing Predictive Accuracy: Assessing the Effectiveness of SVM in Predicting Medical Student Performance." E3S Web of Conferences 465 (2023): 02028. http://dx.doi.org/10.1051/e3sconf/202346502028.

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The high cost of pursuing a medical education necessitates effectively monitoring and evaluating medical students' performance. This study aimed to develop and evaluate a prediction system for medical students’ national exam scores using the Support Vector Machine (SVM) algorithm. The dataset consisted of grades from first and second-year medical students at Muhammadiyah University of Yogyakarta, specifically from the 2014 and 2015 classes, to predict the final year exam score. The methodology involved data acquisition, data preprocessing, and classification and prediction of student performan

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Liu, Laura. "Disease Prediction Models Based on Medical Big Data." Theoretical and Natural Science 63, no. 1 (2024): 139–43. http://dx.doi.org/10.54254/2753-8818/2024.17942.

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The advent of big data technology has heralded a transformative era in healthcare, with significant implications for disease prediction. This review article delves into the integration of medical big data in predictive modeling, highlighting the pivotal role of data preprocessing, feature engineering, and machine learning algorithms. We explore the escalating research interest, as evidenced by an upward trend in academic publications from 2010 to 2023. The paper underscores the advantages of big data analytics in healthcare, leading to more accurate and personalized disease predictions. Furthe

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Wade, Bruce A., Krishnendu Ghosh, and Peter J. Tonellato. "Optimization of a Gene Analysis Application." Computing Letters 2, no. 1-2 (2006): 81–88. http://dx.doi.org/10.1163/157404006777491927.

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MetaGene is a software environment for gene analysis developed at the Bioinformatics Research Center, Medical College of Wisconsin. In this work, a new neural network optimization module is developed to enhance the prediction of gene features developed by MetaGene. The input of the neural network consists of gene feature predictions from several gene analysis engines used by MetaGene. When compared, these predictions are often in conflict. The output from the neural net is a synthesis of these individual predictions taking into account the degree of conflict detected. This optimized prediction

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Melchane, Selestine, Youssef Elmir, Farid Kacimi, and Larbi Boubchir. "Artificial Intelligence for Infectious Disease Prediction and Prevention: A Comprehensive Review." Acta Universitatis Sapientiae, Informatica 16, no. 1 (2025): 160–97. https://doi.org/10.47745/ausi-2024-0010.

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Artificial Intelligence and infectious diseases prediction have recently experienced a common development and advancement. Machine learning apparition, along with deep learning emergence, extended many approaches against diseases apparition and their spread. And despite their outstanding results in predicting infectious diseases, conflicts appeared regarding the types of data used and how they can be studied, analyzed, and exploited using various emerging methods. This has led to some ongoing discussions in the field. This research aims not only to provide an overview of what has been accompli

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Anilkumar, Chunduru, Seepana Kanchana, Sasapu Bharath Kumar, Reddy Pravallika, and Surapureddi Mrudula. "Multi chronic disease prediction: A survey." Applied and Computational Engineering 5, no. 1 (2023): 273–78. http://dx.doi.org/10.54254/2755-2721/5/20230579.

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People today deal with a variety of illnesses as a result of their lifestyle choices and the environment. As a result, many people have chronic diseases that go untreated for long periods of time, imposing a tremendous impact on society. Therefore, predicting disease sooner is becoming a crucial duty. in order to systematically evaluate patients' future disease risks using their medical records. But for a doctor, making an accurate forecast based on symptoms is too challenging. The hardest task is making an accurate diagnosis of a condition. For this problem to be resolved, illness detection r

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Pratama, Matthew. "Utilizing Linear Regression for Predicting Sales of Top-Performing Products." International Journal of Information Technology and Computer Science Applications 1, no. 3 (2023): 174–80. http://dx.doi.org/10.58776/ijitcsa.v1i3.92.

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PT Ajidarma Delta Medika is a company engaged in the sale of medical devices in the city of Bekasi. This company markets a variety of medical device products. Judging from the large number of consumer requests for medical device products based on sales data for the last 3 years, predictions are needed for the best-selling product sales, in order to facilitate the company in planning the supply of stock. To find out the best-selling medical device product sales, data prediction techniques are used with the Linear Regression algorithm. By using the Linear Regression algorithm, the results are ob

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Lu, Yi. "Heart Disease Prediction Model based on Prophet." Highlights in Science, Engineering and Technology 39 (April 1, 2023): 1035–40. http://dx.doi.org/10.54097/hset.v39i.6700.

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Heart disease is one of the major causes of death for people of all races, genders, and nationalities. In the United States, for instance, heart disease causes more than 600,000 deaths every year and is the largest leading cause of death in 2020. A reliable heart diseases mortality prediction model could acknowledge the patients’ medical professionals that the heart disease risk level of the specific group. This approach is significant in preventing further increases in heart disease mortality rates worldwide. Nowadays, multiple Machine Learning (ML) models, including hybrid models produced im

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Csillag, Daniel, Lucas Monteiro Paes, Thiago Ramos, et al. "AmnioML: Amniotic Fluid Segmentation and Volume Prediction with Uncertainty Quantification." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 13 (2023): 15494–502. http://dx.doi.org/10.1609/aaai.v37i13.26837.

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Accurately predicting the volume of amniotic fluid is fundamental to assessing pregnancy risks, though the task usually requires many hours of laborious work by medical experts. In this paper, we present AmnioML, a machine learning solution that leverages deep learning and conformal prediction to output fast and accurate volume estimates and segmentation masks from fetal MRIs with Dice coefficient over 0.9. Also, we make available a novel, curated dataset for fetal MRIs with 853 exams and benchmark the performance of many recent deep learning architectures. In addition, we introduce a conforma

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J., Sirisha,. "LUNG CANCER PREDICTION THROUGH DEEP LEARNING." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem29970.

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Lung cancer remains a leading cause of cancer-related mortality worldwide, emphasizing the critical need for accurate prediction and early detection methods. In recent years, Convolutional Neural Network (CNN) models have emerged as powerful tools for medical image analysis, showing promising results in various diagnostic tasks. Building upon previous research utilizing Artificial Neural Network (ANN) models, this paper presents an in-depth investigation into the application of CNN models for lung cancer prediction using medical imaging data. Leveraging insights from previous ANN-based approac

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Kulkarni, Mukund, Dhammadeep D. Meshram, Bhagyesh Patil, Rahul More, Mridul Sharma, and Pravin Patange. "Medical Insurance Cost Prediction using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 12 (2022): 449–56. http://dx.doi.org/10.22214/ijraset.2022.47923.

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Abstract: Insurance is a policy that helps to cover up all loss or decrease loss in terms of expenses incurred by various risks. A number of variables affect how much insurance costs. These considerations of different factors contribute to the insurance policy cost expression. Machine Learning( ML) in the insurance sector can make insurance more effective. In the domains of computational and applied mathematics the machine learning (ML) is a well-known research area. ML is one of the computational intelligence aspects when it comes to exploitation of historical data that may be addressed in a

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Homaile, Mascarin do Vale, and Elisabete Farinha Ferreira e. Dias Pereira Ana. "Culpability and Prediction in Medical Error." International Journal of Innovative Research in Multidisciplinary Education 03, no. 06 (2024): 1137–47. https://doi.org/10.5281/zenodo.12578418.

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According to the World Health Organization (2023), 2.6 million people die annually due to preventable adverse events in hospitals. This data is still underestimated, as the study evaluated only 150 countries. Each year, out of the 19.4 million people treated in hospitals in Brazil, 1.3 million experience at least one side effect caused by negligence or recklessness during medical treatment. In 2021, the Federal Council of Medicine reported that 92% of colleges do not meet at least one of the three parameters deemed ideal for the proper operation of medical courses. Every hour, three medical er

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Md Mohtaseem Billa. "Medical Insurance Price Prediction Using Machine Learning." Journal of Electrical Systems 20, no. 7s (2024): 2270–79. http://dx.doi.org/10.52783/jes.3962.

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The escalating costs and complexities in the healthcare sector underscore the necessity for efficient predictive models to anticipate medical insurance prices. This study explores the application of machine learning techniques for forecasting medical insurance premiums, aiming to provide stakeholders with invaluable insights for pricing strategies and risk management. Using a comprehensive dataset encompassing demographic information, medical history, lifestyle factors, and insurance coverage details, various machine learning algorithms including regression, decision trees, random forests are

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Elam, C. L., та M. M. Johnson. "Prediction of medical studentsʼ academic performances". Academic Medicine 67, № 10 (1992): S28–30. http://dx.doi.org/10.1097/00001888-199210000-00029.

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van Houwelingen, Hans. "Special Issue: Prediction in Medical Statistics." Statistica Neerlandica 55, no. 1 (2001): 2. http://dx.doi.org/10.1111/1467-9574.00152.

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Nouretdinov, Ilia, Dmitry Devetyarov, Volodya Vovk, et al. "Multiprobabilistic prediction in early medical diagnoses." Annals of Mathematics and Artificial Intelligence 74, no. 1-2 (2013): 203–22. http://dx.doi.org/10.1007/s10472-013-9367-5.

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Lei, Qiyun. "Machine Learning in Medical Insurance Prediction." Advances in Economics, Management and Political Sciences 45, no. 1 (2023): 222–28. http://dx.doi.org/10.54254/2754-1169/45/20230270.

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Nowadays, the trend of an ageing society is more and more obvious. Accompanied with the huge population of the elderly, the medical insurance industry has more prospects and potential. As a result, more service and business operations of insurance companies are in need. With the analysis from past data, computer algorithms help a lot in predicting the new output values, aiding data-driven business decisions, ranking of influential factors and digital computerization. Through machine learning, the insurance companies are able to make a decision flatly in premiums without having unnecessary medi

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Bhavekar, Girish Shrikrushnarao, Pratiksha Vasantrao Chafle, Agam Das Goswami, et al. "Hybrid approach to medical decision-making: prediction of heart disease with artificial neural network." Bulletin of Electrical Engineering and Informatics 13, no. 6 (2024): 4124–33. http://dx.doi.org/10.11591/eei.v13i6.5583.

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Heart disease prediction is important in today’s world because it helps to reduce the unpredictable death rate of patients, and cardiac diseases are considered one of the most serious diseases affecting people. Hence, in this paper, a heart disease prediction model is designed for effective prediction of heart diseases by means of machine learning (ML) and deep learning (DL). This prediction uses the proposed method of an artificial neutral network and the Chi2 feature selection method applied to determine which features from the dataset were suitable for prediction. The proposed methodology u

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Rakhmetullina, Zhenisgul, Saule Belginova, Alibekkyzy Karlygash, Aigerim Ismukhamedova, and Shynar Tezekpaeva. "Research and implementation of the medical text analysis algorithm for predicting mortality." Indonesian Journal of Electrical Engineering and Computer Science 34, no. 3 (2024): 1965. http://dx.doi.org/10.11591/ijeecs.v34.i3.pp1965-1977.

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Mortality prediction has a role to play in the development of a descriptive measure of the quality of care that provides a fair and equitable means of comparing and evaluating hospitals. This article describes a study of a medical text analysis algorithm for mortality prediction that used big data in the form of unstructured medical notes. The article describes the concept of using text mining technology for medical systems, a method for preprocessing medical data to predict patient mortality, an algorithm for predicting patient deaths based on the logistic regression classifier and presents a

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Rakhmetullina, Zhenisgul, Saule Belginova, Alibekkyzy Karlygash, Aigerim Ismukhamedova, and Shynar Tezekpaeva. "Research and implementation of the medical text analysis algorithm for predicting mortality." Indonesian Journal of Electrical Engineering and Computer Science 34, no. 3 (2024): 1965–77. https://doi.org/10.11591/ijeecs.v34.i3.pp1965-1977.

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Mortality prediction has a role to play in the development of a descriptive measure of the quality of care that provides a fair and equitable means of comparing and evaluating hospitals. This article describes a study of a medical text analysis algorithm for mortality prediction that used big data in the form of unstructured medical notes. The article describes the concept of using text mining technology for medical systems, a method for preprocessing medical data to predict patient mortality, an algorithm for predicting patient deaths based on the logistic regression classifier and presents a

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Zhang, Liangqing, Cuirong Yu, Chunrong Jin, et al. "A Remote Medical Monitoring System for Heart Failure Prognosis." Mobile Information Systems 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/406327.

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Remote monitoring of heart disease provides the means to keep patients under continuous supervision. In this paper, we introduce the design and implementation of a remote monitoring medical system for heart failure prediction and management. The three-part system includes a patient-end for data collection, a medical data center as data storage and analysis, and a doctor-end to diagnosis and intervention. The main objective of the system is to prognose the occurrence risk of heart failure (HF) confirmed by the level of N-terminal prohormone of brain natriuretic peptide (NT-proBNP) based on the

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Hatoum, Rima, Ali Alkhazraji, Zein Al Abidin Ibrahim, Houssein Dhayni, and Ihab Sbeity. "Towards a disease prediction system: BioBERT-based medical profile representation." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 2 (2024): 2314. http://dx.doi.org/10.11591/ijai.v13.i2.pp2314-2322.

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<p>Healthcare professionals are increasingly interested in predicting diseases before they manifest, as this can prevent more serious health conditions and even save lives. Machine learning techniques are now playing an important role in healthcare, including in the early prediction of diseases based on prior medical knowledge. However, one of the biggest challenges is how to represent medical information in a way that can be processed by machine learning algorithms. Medical histories are often in a format that computers cannot read, so filtering and converting this information into nume

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Rima, Hatoum, Alkhazraji Ali, Al Abidin Ibrahim Zein, Dhayni Houssein, and Sbeity Ihab. "Towards a disease prediction system: biobert-based medical profile representation." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 2 (2024): 2314–22. https://doi.org/10.11591/ijai.v13.i2.pp2314-2322.

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Predicting diseases in advance is crucial in healthcare, allowing for early intervention and potentially saving lives. Machine learning plays a pivotal role in healthcare advancements today. Various studies aim to predict diseases based on prior knowledge. However, a significant challenge lies in representing medical information for machine learning. Patient medical histories are often in an unreadable format, necessitating filtering and conversion into numerical data. Natural language processing (NLP) techniques have made this task more manageable. In this paper, we propose three medical info

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Spreafico, Marta, Audinga-Dea Hazewinkel, Michiel A. J. van de Sande, Hans Gelderblom, and Marta Fiocco . "Machine Learning versus Cox Models for Predicting Overall Survival in Patients with Osteosarcoma: A Retrospective Analysis of the EURAMOS-1 Clinical Trial Data." Cancers 16, no. 16 (2024): 2880. http://dx.doi.org/10.3390/cancers16162880.

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Since the mid-1980s, there has been little progress in improving survival of patients diagnosed with osteosarcoma. Survival prediction models play a key role in clinical decision-making, guiding healthcare professionals in tailoring treatment strategies based on individual patient risks. The increasing interest of the medical community in using machine learning (ML) for predicting survival has sparked an ongoing debate on the value of ML techniques versus more traditional statistical modelling (SM) approaches. This study investigates the use of SM versus ML methods in predicting overall surviv

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Duraisamy, Balakrishnan, Rakesh Sunku, Krithik Selvaraj, Vishnu Vardhan Reddy Pilla, and Manoj Sanikala. "Heart disease prediction using support vector machine." Multidisciplinary Science Journal 6 (December 15, 2023): 2024ss0104. http://dx.doi.org/10.31893/multiscience.2024ss0104.

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Heart disease prediction through online consultation using machine learning refers to the application of advanced algorithms and techniques to analyze medical data collected during online consultations to predict the likelihood of an individual developing heart disease. Machine learning models are trained using historical data that includes various risk factors such as age, gender, blood pressure, cholesterol levels, and medical history. These models then utilize the input provided by patients during online consultations, such as symptoms, lifestyle habits, and additional medical tests, to gen

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Kim, Minji, Jiseong Byeon, Jihun Chang, and Sekyoung Youm. "Predicting Post-Liposuction Body Shape Using RGB Image-to-Image Translation." Applied Sciences 15, no. 9 (2025): 4787. https://doi.org/10.3390/app15094787.

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The growing interest in weight management has elevated the popularity of liposuction. Individuals deciding whether to undergo liposuction must rely on a doctor’s subjective projections or surgical outcomes for other people to gauge how their own body shape will change. However, such predictions may not be accurate. Although deep learning technology has recently achieved breakthroughs in analyzing medical images and rendering diagnoses, predicting surgical outcomes based on medical images outside clinical settings remains challenging. Hence, this study aimed to develop a method for predicting b

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K., K., and Suribabu Korada. "Disease Prediction Using Machine Learning Approaches Considering Bio-Medical Signal Analysis: A Survey." Fusion: Practice and Applications 19, no. 2 (2025): 315–27. https://doi.org/10.54216/fpa.190223.

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In medical diagnosis and prognosis, symptoms provided by patients play a critical role in identifying diseases. Machine learning offers a powerful approach to analyzing and predicting illnesses based on these symptoms. In particular, classification algorithms are widely used to analyze input data and predict disease outcomes. A key factor in effective classification is the selection of relevant attributes, which directly affects the accuracy of the prediction. This research emphasizes the importance of proper feature extraction techniques in the context of disease prediction using biomedical s

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