Machine Learning-Based Method of Cardiovascular Disease Classification based on Objective, Examination, and Subjective Features

cardiovascular disease (CVD) is among the leading causes of worldwide death, which is why the essential role of early diagnosis and accurate risk estimation in modern healthcare is difficult to overestimate. The significant digitization of patient health records has enabled the implementation of machine-learning techniques to supplement clinical decision-making with data-based and evidence-based insights. The current study presents a machine-learning-based CVD classification framework that combines the objective demographic features, clinical examination findings and non-objective lifestyle-related factors. The data used in this study includes those of the patients taken at the regular medical check-ups, and they include age, gender, systolic and diastolic blood pressure, lipids profile, glucose level, body mass index, and lifestyle habits of the patients like use of tobacco, alcohol, and exercise. A comprehensive exploratory data analysis was conducted to clarify the distribution of features, inter-variable relationships and possible potential risk factors relevant to CVD. Various machine-learning models such as the Logistic Regression, the K-Nearest Neighbors, the Support Vector Machines, the Decision tree, the random forest, the Gradient Boosting and other boosting-based ensemble models were applied and evaluated systematically. When it came to measuring model performance, accuracy, precision, recall, F1-score, confusion matrices, and area under the receiver operating characteristic curve (ROC-AUC) were used. The experimental findings indicate that the ensemble-based classifiers are better than single traditional model as they have high predictive accuracy and robustness. These results confirm the timely impact of clinical and lifestyle variables on the prediction of CVD risk and justify the effectiveness of the machine-learning approaches as auxiliary tools in the early diagnosis of the disease and its stratification of risk.