A Multi-Disease Detection System Using Machine Learning: A Case Study on Diabetes, Parkinson’s, and Heart

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Manali Sutariya; Rauky Yadav

A Multi-Disease Detection System Using Machine Learning: A Case Study on Diabetes, Parkinson’s, and Heart

Authors: Manali Sutariya, Rauky Yadav

Unique Paper ID: 175360
Volume: 11
Issue: 11
PageNo: 2454-2458

Keywords: No Keywords Found

Abstract:
In recent years, the integration of Machine Learning (ML) techniques in the healthcare sector has significantly enhanced the accuracy and efficiency of disease diagnosis. This paper presents a unified ML-based system designed to detect three major chronic diseases: Diabetes, Parkinson’s Disease, and Heart Disease. Utilizing publicly available datasets from trusted repositories, the system applies a comparative analysis of various ML algorithms including Logistic Regression, Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and Random Forest. The models were trained and evaluated using performance metrics such as accuracy, precision, recall, and F1-score. Experimental results demonstrate that ensemble-based approaches like Random Forest consistently yield higher predictive performance across all datasets. The proposed system aims to assist medical professionals in early diagnosis and decision-making, ultimately improving patient outcomes. Future enhancements may include deep learning integration and real-time prediction through web or mobile deployment.

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Cite This Article

ISSN: 2349-6002
Volume: 11
Issue: 11
PageNo: 2454-2458

A Multi-Disease Detection System Using Machine Learning: A Case Study on Diabetes, Parkinson’s, and Heart

Available:https://ijirt.org/article?manuscript=175360

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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