Transforming healthcare: Harnessing machine learning for early disease prediction in breast cancer, cardiovascular, and heart diseases

Modern healthcare struggles to forecast and diagnose key diseases like breast cancer, heart disease, and cardiovascular disease. Early management reduces mortality and improves patient outcomes for these illnesses, which are leading causes of death worldwide. Traditional diagnostic approaches take time, need expert interpretation, and are prone to inaccuracy. By quickly processing huge amounts of clinical and biomedical data, machine learning (ML) can improve diagnostic accuracy. Random Forest, Support Vector Machine (SVM), Decision Tree, and XGBoost are tested for disease prediction in this paper. Mutual Information and Correlation Coefficient were used to choose the most important features to optimize model performance. The most accurate classifiers were Random Forest and XGBoost, which predicted heart disease 99% and breast cancer 96%. Machine learning can improve healthcare by enhancing diagnostic precision, especially when combined with feature selection approaches. These technologies could change diagnosis, enabling earlier interventions, more individualized therapies, and improved healthcare results worldwide.