CARDIO PREDICT: HARNESSING MACHINE LEARNING FOR ADVANCED HEART DISEASE RISK ASSESSMENT

Abstract

Heart disease prediction using machine learning algorithms has gained significant attention due to its potential to improve diagnosis and treatment. This study explores various machine learning techniques and an algorithm applied to heart disease prediction. We analyze the performance of popular algorithms such as logistic regression, decision trees, random forests, support vector machines, and artificial neural networks on heart disease datasets. Additionally, we investigate the impact of feature selection, data preprocessing techniques, and model evaluation metrics on the predictive performance. The results demonstrate the heart disease risk, providing valuable insights for medical practitioner and researchers in the field of Cardiovascular health. The datasets used comprises a collection of patient data, including age, gender, blood pressure, cholesterol levels, and other relevant medical indicators. Neural networks are trained and evaluated to assess their performance in predicting the presence investigates the impact feature selection hyper parameter tuning. The results obtained provide insights into the strengths and limitations of different machine learning approaches for heart disease prediction, offering valuable guidance for healthcare practitioners and researchers in the field. Heart disease is prevalent and life-threatening condition worldwide. We analyze the performance of these algorithms using relevant metrics such as accuracy, precision, recall, and Fr-score. Additionally, we investigate feature importance to understand the factors contributing most to heart disease prediction. Our findings demonstrate the potential of machine learning in assisting healthcare professionals in early detection and prevention of heart disease, ultimately improving penitent outcomes and quality of life.