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

Abstract

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.