Artificial Intelligence in Medicine for Chronic Disease Classification Using Machine Learning
Mekhriddin Rakhimov, Ravshanjon Akhmadjonov, Shakhzod Javliev
Abstract
Artificial intelligence (AI) systems in medicine are one of the most important modern trends in global healthcare. Artificial intelligence technologies are fundamentally changing the global healthcare system, making it possible to radically rebuild the system of medical diagnostics while reducing healthcare costs. AI is actively used in research to develop methods for diagnosing coronary heart disease (CHD). There are different types of CHD. Before treating a disease, it is necessary to determine which class of diseases it belongs to. Based on the feature space of the disease, it is possible to classify the type of CHD. Machine learning algorithms can solve this problem. The k-nearest neighbors (KNN) algorithm is a simple, easy-to-implement supervised machine learning algorithm that can be used to solve classification problems. The dataset is the more important part of the supervised machine learning algorithm for training. Gathering data is the most important step in solving any supervised machine learning problem. But choosing more important part from the collected data is one of the tasks to be solved. The main purpose of this study is to select more useful parametric attributes from the dataset to obtain a high F1-score of CHD classification.