Manuscript Number : SHISRRJ247252

Although cardiovascular diseases (CVDs) tend to be a significant global cause of mortality and morbidity, it is vital that fresh tactics for early detection and intervention be investigated. In response, this study presents a comprehensive machine learning framework designed to predict the presence of heart disease in individuals using a rich dataset encompassing diverse health indicators, including age, sex, chest pain, cholesterol levels, fasting blood pressure, heart rate, electrocardiographic results, and thalassemia status. Our strategy uses quite a few of methods that employ machine learning, comprising For physicians to precisely recognize heart disease warning signs, tricky trends and correlations are extracted from the data thru logistic regression, decision trees, random forests, support vector machines, and neural networks.. Leveraging heretofore disclosed datasets as benchmarks, consuming cross-validation and external validation, indicators of performance like as accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC-ROC) will be leveraged to in-depth assess the power of the floated tool. Beyond its predictive capabilities, this project emphasizes the interpretability of the model's outputs, shedding light on the relative importance of various features in influencing heart health. and area under the receiver operating characteristic curve (AUC-ROC) will be adopted to impartially assess the clinical efficacy of the floated tool. The successful implementation of this project holds immense potential to revolutionize public health efforts by offering a reliable, accessible, and scalable tool for early detection of heart disease. By bridging the gap between machine learning and clinical practice, our approach not only advances the state-of-the-art in predictive modelling but also addresses a pressing societal need, paving the way for proactive strategies to mitigate the burden of CVDs and improve patient outcomes on a global scale.