Enhancing Cardiovascular Disease Diagnosis through Data-Driven Feature Analysis and Cross-Validated Machine Learning Models
Abstract - Cardiovascular diseases are a major global health problem, accounting for 17.9 million deaths per year and constituting 32 percent globally. According to the World Health Organization, the disease in people is due to an unhealthy diet,such as the intake of more junk food along with alcohol and smoking consumption; high cholesterol levels; and a reduction in people 's lives. And the diagnostic techniques that are used ,like ECG, echocardiography, angiography, and various laboratroy test and are very costly and time-consuming. It will be in limited in rural areas, so it will be as the limited no of infrastructure, and their will be a delay of detection of disease and the increases of chances of heart attack and strokes. In this study, we used machine learning to produce digital records,predict heart disease, reduce human errors, and improve decision-making. The methodology used the data set from Excel and Panda, using missing values and conducting the analyses'visualization of gender-based frequency and tabulating the variables chest pain and diagnosis and evaluating the scatter plots and heart risk, which will be associated with a risk pattern. Now, we can analyze with a correlation matrix, manage redundancy, and assist in feature selection. The development of the model is now divided into training and testing sets, 60 percent and 70 percent, and includes and now compares the logistic regression, K-Nearest Neighbors, and Random Forest models using F1-score and ROC-AUC, and we have a cross-validation matrix. And to achieve cross-validation and hyperparameters, such as GridSearchCV or RandomizedSearchCV, which will be included for optimization and will be a combination of ML variables, such as blood pressure, cholesterol levels, chest pain,and blood sugar level, and also to enhance the accuracy and improve the interpretability through features and for CVD and better outcomes and also the limitations on it.