Forecasting Cardio Vascular Diseases Using Kernel Machine

Cardiovascular disorders, including various heart diseases, have emerged as leading causes of mortality worldwide, necessitating accurate and effective diagnostic techniques. Leveraging data science approaches, particularly machine learning methods, offers a solution for analyzing large-scale medical datasets to enable early identification and proper care of these conditions. This study aims to utilize predictive modeling, including Support Vector Machines (SVM), Naïve Bayes, K-Nearest Neighbors (KNN), and Random Forest, to analyze extensive medical records and datasets relevant to heart problems. The objective of this research study is to develop reliable prediction models that can enhance patient care and optimize healthcare processes by incorporating various characteristics such as age, gender, and biochemical markers. This study compares the effectiveness of Random Forest, KNN, Naïve Bayes, and SVM algorithms to determine the best method for estimating cardiac disease. Each algorithm's performance will be closely scrutinized to assess its ability to identify early indicators of cardiovascular diseases, ensuring that accuracy and other relevant evaluation metrics are carefully considered. The ultimate goal of this study is to equip medical professionals with advanced tools capable of quickly identifying and mitigating cardiovascular risks. This will not only improve patient outcomes but also alleviate the burden on healthcare systems.