Review of Machine Learning Algorithm in Medical Health

Subject Areas : Machine learning

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

1 - Department of Computer Engineering, Ard.C., Islamic Azad University, Ardabil, Iran
2 - Department of Computer Engineering, Ard.C., Islamic Azad University, Ardabil, Iran
3 - Department of Computer Engineering, ST,C., Islamic Azad University, Tehran, Iran
4 - Department of Computer Engineering, Ard.C., Islamic Azad University, Ardabil, Iran
5 - Department of Computer Science and Mathematics, CT.C., Islamic Azad University, Tehran, Iran
6 - Department of Computer Engineering, Germi.C., Islamic Azad University, Germi, Iran
7 - Department of Computer Engineering, Ard.C., Islamic Azad University, Ardabil, Iran
8 - Department of Computer Engineering, Ard.C., Islamic Azad University, Ardabil, Iran
9 - Department of Computer Engineering, Ard.C., Islamic Azad University, Ardabil, Iran

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.