A Machine-Learning Approach to Detect Heart Disease

Moreover, heart disease has kept on being the leading cause of death worldwide, hence the importance of coming up with different strategies for early detection and diagnosis. Machine learning algorithms have been found to be very efficient in diagnosing various cardiac diseases, through approaches such as Support Vector Machines (SVM), Random Forest, Neural Networks, and Logistic Regression. The materials of this research consist of a dataset that has various health markers, such as age, blood pressure, cholesterol level, and other clinical factors that are relevant to this issue. The Random Forest classifier was the only one with an incredible accuracy of 97.5% to outsmart the rest of the algorithms used. It also managed to record a 0.998 Area Under the Curve (AUC) score and F1, Precision, and Recall scores of $\mathbf{9 7. 5 \%}$. This finding highlights how machine learning models can help increase diagnostic accuracy and assist healthcare professionals in making better-informed decisions about patient management. In addition to contributing to the knowledge base on heart disease identification, this study also supports using innovative computing techniques in the clinics. By incorporating machine learning, we can significantly enhance early detection levels, enhancing patient outcomes and reducing healthcare expenses. This research formed a basis for subsequent studies concerning the effect of artificial intelligence on cardiovascular wellness