Performance Evaluation of Different Machine Learning Classification Algorithms for Disease Diagnosis

Performance Evaluation of Different Machine Learning Classification Algorithms for Disease Diagnosis

Munder Abdulatef Al-Hashem, Ali Mohammad Alqudah, Qasem Qananwah
Copyright: © 2021 |Pages: 28
DOI: 10.4018/IJEHMC.20211101.oa5
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Abstract

Knowledge extraction within a healthcare field is a very challenging task since we are having many problems such as noise and imbalanced datasets. They are obtained from clinical studies where uncertainty and variability are popular. Lately, a wide number of machine learning algorithms are considered and evaluated to check their validity of being used in the medical field. Usually, the classification algorithms are compared against medical experts who are specialized in certain disease diagnoses and provide an effective methodological evaluation of classifiers by applying performance metrics. The performance metrics contain four criteria: accuracy, sensitivity, and specificity forming the confusion matrix of each used algorithm. We have utilized eight different well-known machine learning algorithms to evaluate their performances in six different medical datasets. Based on the experimental results we conclude that the XGBoost and K-Nearest Neighbor classifiers were the best overall among the used datasets and signs can be used for diagnosing various diseases.
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2. Literature Review

In this work, six different diseases will be classified utilizing the available database. These diseases were addressed previously by many researchers. The related works related to these diseases is presented in the following review:

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