Utilisation of Machine Learning Techniques in Testing and Training of Different Medical Datasets


  • Maad M. Mijwil Computer Techniques Engineering Department, Baghdad College of Economic Sciences University, Baghdad, Iraq https://orcid.org/0000-0002-2884-2504
  • Israa Ezzat Salem Computer Techniques Engineering Department, Baghdad College of Economic Sciences University, Baghdad, Iraq
  • Rana A. Abttan Computer Techniques Engineering Department, Baghdad College of Economic Sciences University, Baghdad, Iraq




Disease, Machine Learning Techniques, COVID-19, Symptoms, Medical Datasets


On our planet, chemical waste increases day after day, the emergence of new types of it, as well as the high level of toxic pollution, the difficulty of daily life, the increase in the psychological state of humans, and other factors all have led to the emergence of many diseases that affect humans, including deadly once like COVID-19 disease. Symptoms may appear on a person, and sometimes they may not; some people may know their condition, and others may neglect their health status due to lack of knowledge that may lead to death, or the disease may be chronic for life. In this regard, the author executes machine learning techniques (Support Vector Machine, C5.0 Decision Tree, K-Nearest Neighbours, and Random Forest) due to their influence in medical sciences to identify the best technique that gives the highest level of accuracy in detecting diseases. Thus, this technique will help to recognise symptoms and diagnose them correctly. This article covers a dataset from the UCI machine learning repository, namely the Wisconsin Breast Cancer dataset, Chronic Kidney disease dataset, Immunotherapy dataset, Cryotherapy dataset, Hepatitis dataset and COVID-19 dataset. In the results section, a comparison is made between the execution of each technique to find out which one is the best and which one is the worst in the performance of analysis related to the dataset of each disease.

Author Biography

Maad M. Mijwil, Computer Techniques Engineering Department, Baghdad College of Economic Sciences University, Baghdad, Iraq

Maad M. Mijwil received B.Sc. degree in Software Engineering from Software Engineering Department at Baghdad College of Economics Sciences University, Iraq in 2008/2009 and M.Sc. degree in Wireless sensor network of computer science from University of Baghdad, Iraq in 2015. Currently he is working Assistant Lecturer at Baghdad College of Economics Sciences University.


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How to Cite

Mijwil, M. M., Salem, I. E. . ., & Abttan, R. A. . (2021). Utilisation of Machine Learning Techniques in Testing and Training of Different Medical Datasets. Asian Journal of Computer and Information Systems, 9(4). https://doi.org/10.24203/ajcis.v9i4.6765