Paper ID:250655W1
Authos: Yashodhan Gupta, Onkar Awate, Sejal Pawar, Chetan. N. Aher and Sanika Bhalerao
Title: Integrated Analytics and Prediction of Heart Disease, Diabetes, and Parkinson’s Using Logistic Regression and SVM Models
Publisher: Algorithm_Lab.
Conference: ICAITD 2025: The Second International Conference of AI new Technology and open Discussion
Location: Pleasanter Lounge Nakano, Tokyo Japan
Date: 1-4 june 2025
Editor: Kazuo Ohzeki (Algorithm_Lab. Professor Emeritus of Shibaura Institute of Technology, Professor of Emeritus of International Professional University of Technology in Tokyo)
Citation: Proceedings: ICAITD 2025
https://doi.org/10.63211/j.p.25.645331
pages: 73-78
Abstract:
Chronic illnesses like diabetes, heart disease, and Parkinson's require early diagnosis for proper management and better health outcomes. This paper introduces an AI-based platform that forecasts the risk of these diseases based on machine learning models. Support Vector Machines (SVM) are used for precise detection of Parkinson's and diabetes, whereas Logistic Regression is used for effective heart disease prediction. SVM model resulted in 78% accuracy in predicting diabetes, and Logistic Regression provided 85% accuracy for heart disease prediction. SVM attained an accuracy of 87% in predicting Parkinson's disease, and it was able to successfully implement intricate pattern identification. Using a user-friendly interface, users are able to enter health data to get real-time accurate predictions, allowing proactive management of health. The integration of sophisticated machine learning algorithms by the system guarantees high accuracy and reliability in disease prediction, with SVM leading in pattern classification for intricate patterns and Logistic Regression offering strong interpretability. Through the minimization of dependence on conventional diagnostic techniques, this platform supports preventive healthcare, enabling users through actionable insights and early intervention to improve overall healthcare outcomes.
Keywords: Keywords: Chronic Disease Prediction, Diabetes, Heart Disease, Parkinson's, Machine Learning, Support Vector Machines (SVM), Logistic Regression, Preventive Healthcare, AI in Healthcare, Early Diagnosis, Disease Risk Assessment
Category: Workshop Papers (IW-AITD 2025 is a joint workshop with ICAITD 2025)
Review process: Committee review
Publication date: July 2nd 2025
First received date: April 26th 2025
Copyright :Author(Full), AlgorithmLab.(First-in-the-world publishing rights granted by the author as the Prceedings of ICAITD 2025)
Licence:Viewer can download and view this review paper, but cannot secondary distribute (redistribute) it. (It is not Creative Commons License, nor MIT Licence) In other words, "Do not distribute" is the License
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