Efficient Cardiovascular Disease Prognosis Through Compound Machine Learning Methods

Abstract: Cardiovascular disease is a serious medical illness worldwide, and it causes hundreds of thousands of deaths annually. Thus, more precise and predictive prognostic technologies are needed to assist physicians to diagnose and treat the patients more effectively. With an attempt to improve overall accuracy to an even greater extent, in this paper we utilize various computer learning approaches to propose an effective way to predict cardiovascular diseases. Traditional approaches used by physicians to recognize cardiac issues are time-consuming and even intuition-based in nature, and therefore they make errors in some cases. To counteract this, we created a smart system that analyzes medical data to discover patterns of cardiovascular disease. Our model combines the strength of various algorithms—Support Vector-Machine-(SVM), Random Forest-(RF), as well as Gradient Boosting-(GB)—into one ensemble system by a voting system to make more precise predictions.We trained and tested the model on a well-known dataset with patient information such as age, blood pressure, cholesterol, heart rate, and ECG results. Before developing the model, we preprocessed the data by replacing missing values, normalizing inputs, and choosing the most informative features to consider.We employed a variety of evaluation metrics, such as precision, recall, accuracy, F1-scoring as well as AUC-ROC rating, to evaluate our system. By achieving an impressive accuracy score of 94.5%, precise of 95.2%, and an AUC score of 0.96, the ensemble model outperformed single approaches. These findings indicate that it is much improved in prediction accuracy as well as empowered to make better medical decisions when more than one artificial intelligence approach is employed. We are of the opinion that this type of system can be an economic device for the early diagnosis of heart disease. In the near future, we intend to improve and scale up this model for real-time use in health care and apply it for the diagnosis of other cardiac diseases as well. Our approach can be a valuable tool in the development of preventive care and smart health technologies. Keywords: Heart Disease Prediction, Machine Learning, Hybrid Algorithms, Ensemble Learning, SVM, Random Forest, Gradient Boosting, Classification, Healthcare Analytics, Predictive Modeling.