Machine Learning Based Prediction of Diabetes Heart Liver Kidney Breast Cancer and Parkinsons Disease

Abstract

Chronic diseases like diabetes and heart, liver, and kidney diseases, as well as Parkinson's and breast cancer, contribute to an increasing burden to the global healthcare industry. Thus, there is a need for sophisticated predictive systems that can proactively and accurately diagnose these conditions. This paper explores how machine learning can assist in predicting high disease risk based on available demographic and clinical data and patient histories. Key techniques such as Support Vector Machines (SVM), Logistic Regression (LR), and Decision Trees (DT), as well as Random Forest (RF), an ensemble technique, will be used to analyze and classify data.To retain the model's interpretability, important predictors will be identified using feature selection.The models will be evaluated based on accuracy, precision, recall, and F1 score to recommend a suitable option for each disease type. It will be shown that machine learning will reshape the diagnosis and prognosis of diseases, building a foundation for timely and individualized medical interventions. Data-driven healthcare as an approach to optimize patient care and alleviate patient care demands will be demonstrated.