Classification of Diseases Using Machine Learning Algorithms: A Comparative Study

Marco-Antonio Moreno-Ibarra, Yenny Villuendas-Rey, Miltiadis D. Lytras, Cornelio Yáñez-Márquez and Julio-César Salgado-Ramírez
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Marco-Antonio Moreno-Ibarra: Centro de Investigación en Computación, Instituto Politécnico Nacional, Mexico City 07700, Mexico
Yenny Villuendas-Rey: Centro de Investigación en Computación, Instituto Politécnico Nacional, Mexico City 07700, Mexico
Miltiadis D. Lytras: Effat College of Engineering, Effat University, P.O. Box 34689, Jeddah 21478, Saudi Arabia
Cornelio Yáñez-Márquez: Centro de Investigación en Computación, Instituto Politécnico Nacional, Mexico City 07700, Mexico
Julio-César Salgado-Ramírez: Ingeniería Biomédica, Universidad Politécnica de Pachuca, Pachuca 43380, Mexico

Mathematics, 2021, vol. 9, issue 15, 1-21

Abstract: Machine learning in the medical area has become a very important requirement. The healthcare professional needs useful tools to diagnose medical illnesses. Classifiers are important to provide tools that can be useful to the health professional for this purpose. However, questions arise: which classifier to use? What metrics are appropriate to measure the performance of the classifier? How to determine a good distribution of the data so that the classifier does not bias the medical patterns to be classified in a particular class? Then most important question: does a classifier perform well for a particular disease? This paper will present some answers to the questions mentioned above, making use of classification algorithms widely used in machine learning research with datasets relating to medical illnesses under the supervised learning scheme. In addition to state-of-the-art algorithms in pattern classification, we introduce a novelty: the use of meta-learning to determine, a priori, which classifier would be the ideal for a specific dataset. The results obtained show numerically and statistically that there are reliable classifiers to suggest medical diagnoses. In addition, we provide some insights about the expected performance of classifiers for such a task.

Keywords: meta-learning; supervised classifiers; medical datasets; data complexity (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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