Evaluation of cardiovascular disease in diabetic patients using machine learning techniques

Abstract

Machine learning (ML) improves operations in many industries, including medicine. It affects the prognosis of several disorders, including heart disease. If predicted, it may provide doctors with new insights and allow them to treat each patient individually. If anticipated, it may provide medical practitioners with valuable information. Our team uses machine learning algorithms to study heart disease risk. This research will compare decision trees, AdaBoost, support vector machines, artificial neural networks (ANN), and customized ANN. The study will include this analysis. The given model will leverage the dataset of general information and medical test results. Our model uses particle swarm optimization (PSO) and k-nearest neighbors (KNN). Algorithm for feature selection. The model reduces dimensionality using evolutionary algorithms and neural networks. We compared the numerous assessment criteria to the current models, our model, and earlier models. Because of this, the suggested model's suitability was rated with the highest accuracy.