EMPLOYMENT OF THE SELECTIVE MACHINE LEARNING ALGORITHM FOR THE EARLY AND EFFECTIVE DETECTION AND DIAGNOSIS OF CARDIOVASCULAR DISEASE

Heart and blood artery dysfunction is the root cause of cardiovascular disease, which includes coronary heart disease, cerebrovascular disease, peripheral arterial disease, rheumatic heart disease, deep vein thrombosis, and pulmonary embolism. A model for using a machine-learning algorithm to find cardiovascular diseases is presented in this paper. Utilized the agile methodology in this research, planning, requirements analysis, designing, coding, testing, and documentation are all carried out simultaneously throughout the stages of the production process. Using four distinct machine learning algorithms—a Support Vector Classifier, a K-Nearest Neighbors Classifier, a Random Forest Classifier, and a Decision Tree Classifier—the patient dataset is used to train the model in this paper. An algorithm will make the predictions, resulting in the most accurate results. Flask, a web-based implementation of this model, was used to make a prediction, the user must fill in 13 inputs on the web. Flask and the Python programming language are used to implement the model and the machine learning algorithms. We use a K-Nearest Neighbors Classifier algorithm after considering all four machine learning algorithms. The prediction has a good accuracy of 85.83 per cent, which is good for any model.