Cardiovascular Ailment Prediction Machine Learning: A new era

Heart disease is a serious disease that affects the heart. Since heart disease can be life-threatening, researchers are focusing on developing smart systems to accurately diagnose heart disease based on electronic medical records with the help of machine learning algorithms. This study presents several machine learning methods to predict heart disease using information about patients’ vital health status. This article introduces four classification methods: Multilayer Perceptron (MLP), Support Vector Machine (SVM), Random Forest (RF), and Naive Bayes (NB) for building predictive models. Complete the data preprocessing and feature selection steps before building the model. Today, machine learning (ML) plays an important role in disease prediction and is an important tool in medicine. In this research, study-based analysis was planned. Our aim is to create a good prediction model to predict cardiovascular diseases; This allows better SMOTE technology to show better results.. This study collected data to predict heart disease. It evaluates the accuracy of various machine learning techniques, including Naive Bayes, Random Forests, Decision Trees, Logistic Regression, Support Vector Machines, K-Nearest, and Gradient Boosting. Cardiovascular disease prediction; includes data comprehension, data analysis, and architecture. Researchers use heart disease data to great effect.