Multi-Disease Prediction System using Machine Learning

The advancement of Machine Learning (ML) in healthcare has facilitated significant improvements in disease diagnosis and early detection. Early diagnosis of diseases is crucial for improving treatment success rates and reducing mortality. In this paper, we propose a Multi-Disease Prediction System that leverages various ML algorithms to predict the presence of Diabetes, Heart Disease, Parkinson’s Disease, Breast Cancer, and Lung Cancer. The system utilizes Support Vector Machines (SVM) and Logistic Regression classifiers to process patient data and predict disease outcomes with high accuracy. Our results show that the system achieves a testing accuracy of 94.29% for Breast Cancer and 90.38% for Diabetes, showcasing its potential as an effective diagnostic tool. The paper discusses the design, implementation, evaluation as well as deployment of the system, and its effectiveness in predicting diseases with high accuracy. It also discusses the challenges faced in disease prediction, the comparative performance of various algorithms, and future improvements that can be made to enhance the accuracy of the system and its usability. The results demonstrate that the system can be an essential tool for healthcare professionals in early detection and decision-making processes.