Heart disease prediction using Machine Learning

Heart disease continues to pose a significant global health challenge, underscoring the importance of reliable predictive tools for early detection and intervention.This research employs machine learning techniques to forecast the probability of heart disease using a range of clinical attributes.The dataset comprises demographic information, clinical observations, and laboratory results collected from individuals undergoing diagnostic procedures.Data preprocessing steps, including managing missing values and standardizing features, were implemented to prepare the data for analysis.Four machine learning algorithms-Decision Trees, Support Vector Machines (SVM), Logistic Regression, and K-Nearest Neighbors (KNN)-were utilized and evaluated.Performance metrics such as accuracy, precision, recall, and F1-score were calculated to assess the models' predictive capabilities.Additionally, confusion matrices and Receiver Operating Characteristic (ROC) curves were employed to visually represent the models' classification accuracy.The findings indicate promising predictive accuracy across all algorithms, with Logistic Regreesion exhibiting the highest performance based on the assessed metrics.This study underscores the potential of machine learning to augment traditional diagnostic approaches for heart disease and emphasizes the importance of further exploration in this domain.In this study, the logistic regression model demonstrated a notable accuracy of 88% in predicting the likelihood of heart disease, indicating its strong performance.