Chronic disease prediction using data mining algorithms

• Published in: AIP conference proceedings Vol. 3075; no. 1
• Main Authors: Anand, Harsh, Prakash, Shashwat, Sandhia, G. K., Prabha, J. Rama
• Format: Journal Article Conference Proceeding
• Language: English
• Published: Melville American Institute of Physics 29.07.2024
• Subjects: Accuracy; Algorithms; Chronic illnesses; Data mining; Datasets; Disease; Heart diseases; Kidney diseases; Machine learning; Support vector machines
• ISSN: 0094-243X; 1551-7616
• DOI: 10.1063/5.0217069

This research paper proposes a project based on chronic disease prediction, which includes Heart disease, kidney disease, diabetes, and breast cancer disease prediction using Data Mining algorithms. To achieve better accuracy compared to existing systems, a dataset for each disease will be utilized, and multiple Data Mining algorithms, including Logistic regression, Support vector machine, Naive Bayes, Random forest, and k-nearest neighbor algorithms, will be tested on each disease dataset for comparision of better accuracy. Based on the results, the algorithm with the highest accuracy will be selected for project. The user will be required to provide their symptoms, and our system will predict whether the user is suffering from any listed disease or not. The motivation behind this project is to create a more accurate and efficient system for disease prediction, which can help in early detection and prevention of chronic diseases. The study’s main findings highlight that the proposed machine learning-based system can achieve high accuracy in disease prediction. This suggests its potential as a valuable tool for healthcare professionals and individuals to effectively monitor and manage chronic diseases.