Cardio Predict: A Smart System for Predicting Heart Disease

cardiovascular disease is the leading disease globally, and hypertension, diabetes, among other lifestyle factors contribute to it. Traditional diagnostics mainly focus on retrospective biometric assessment; such diagnostics will not offer sufficient prediction capabilities for proactive management without invasive procedures. In this paper, we propose a machine learning-based risk stratification from noninvasive, age-based assessments aiming at early detection of cardiovascular disease risks in asymptomatic people. This was made possible by leveraging a comprehensive dataset from Kaggle that was used in evaluating different models of ML over the given data with high emphasis laid on logistic regression because, unlike random forest and KNN models, this has exemplary predictive capability. In summation, what this piece of research is able to contribute is an identification of a good non-invasive robust predictive approach that’s likely to be applied for use in the clinical space in improving effectiveness in earlier intervention as well as that of patients’ outcome.