Designing of Heart Disease Prediction Model Using Machine Learning Algorithm

Heart disease remains one of the leading causes of death worldwide. Effective management and prevention heavily depend on early detection and accurate prediction. However, traditional approaches have struggled to improve classification performance for heart disease. This research proposes a heart disease prediction model that leverages machine learning algorithms to enhance early diagnosis and preventive measures. The process begins with the preprocessing of a patient dataset, containing labelled instances of heart disease presence or absence. Logistic regression is then applied and assessed using this dataset. The model exhibits strong accuracy and reliability in predicting heart disease risk, providing valuable insights for clinical decision- making and improving patient care outcomes. The research presented in the paper is helpful in achieving SDG3: Good Health and Well-Being.