PREDICTING HEART DISEASE USING MACHINE LEARNING ALGORITHMS
Berdaly A.K. Abdiahmetova Z.M.
26 September 2022al-Farabi Kazakh State National University
KazNU Bulletin. Mathematics, Mechanics, Computer Science Series
2022#115Issue 3101 - 111 pp.
Increasing the accuracy of detecting heart disease is widely studied in the field of machine learning. Such study is intended to prevent large costs in the field of healthcare and is the reason for the misdiagnosis. As a result, various methods of analyzing disease factors were proposed, aimed at reducing differences in the practice of doctors and reducing medical costs and errors. In this study, 6 classification learning algorithms were used, including machine learning methods such as classification Tree, Close neighborhood method, Naive Bayes, Random forest tree, and Busting methods. These methods were collected by the University of Cleveland. Using heart.csv dataset, they were trained to make an effective and accurate prediction of heart disease. In order to increase the predictive capabilities of algorithms, all methods were trained primarily on non-standardized data. A study was conducted on how much data standardization affects the result using the Standard Scaler method. In the paper, this method helped algorithms such as KNN and SVC improve the result about 25%.
Busting , Classification , Confusion Matrix , Metrics , Standardization , Training Selection
Text of the article Перейти на текст статьи
Al-Farabi Kazakh National University, Almaty, Kazakhstan
Al-Farabi Kazakh National University
10 лет помогаем публиковать статьи Международный издатель
Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026