Review of Machine Learning Algorithm in Medical Health

Subject Areas : Machine learning

Zahra Ghorbani ¹ , Sahar Behrouzi-Moghaddam ² , Shahram Zandiyan ³ , Nasser Mikaeilvand ⁴ , babak nouri moghadam ⁵ , Sajjad Jahanbakhsh Gudakahriz ⁶ , Ailin Khosravani ⁷ , fatemeh Tahmasebizade ⁸ , Abbas Mirzaei ^{9 *}

1 - دانشگاه آزاد اسلامی واحد اردبیل
2 - گروه مهندسی کامپیوتر، واحد اردبیل، دانشگاه آزاد اسلامی، اردبیل، ایران
3 - گروه علوم کامپیوتر و ریاضیات، واحد تهران مرکز، دانشگاه آزاد اسلامی، تهران، ایران
4 - Department of Mathematics, Central Tehran Branch, Islamic Azad University, Tehran, Iran
5 - Faculty of Computer Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
6 - Department of Computer Engineering, Ardabil Branch, Islamic Azad University, Ardabil, Iran
7 - گروه مهندسی کامپیوتر، واحد اردبیل، دانشگاه آزاد اسلامی، اردبیل، ایران
8 - گروه مهندسی کامپیوتر، واحد اردبیل، دانشگاه آزاد اسلامی، اردبیل، ایران
9 - دانشگاه آزاد اسلامی

Received: 2025-04-13 Accepted : 2025-06-20 Published : 2025-06-30

Keywords: Machine Learning, Medical, Supervised Learning, Classification,

Abstract :

Recently, health-related data has been analyzed using a variety of cutting-edge methods, including artificial intelligence and machine learning. The application of machine learning technologies in the healthcare industry is enhancing medical professionals' proficiency in diagnosis and treatment. Researchers have extensively used medical data to identify patterns and diagnose illnesses.  Nevertheless, little research has been done on using machine learning algorithms to enhance the precision and usefulness of medical data. An extensive analysis of the many machine learning methods applied to healthcare applications is given in this work. We first examine supervised and unsupervised machine learning techniques, and then we investigate the applicability of time series tasks on historical data, evaluating their appropriateness for datasets of varying sizes.

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