TY - JOUR
T1 - Classification of diseases using machine learning algorithms
T2 - A comparative study
AU - Moreno-Ibarra, Marco Antonio
AU - Villuendas-Rey, Yenny
AU - Lytras, Miltiadis D.
AU - Yáñez-Márquez, Cornelio
AU - Salgado-Ramírez, Julio César
N1 - Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2021/8
Y1 - 2021/8
N2 - 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.
AB - 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.
KW - Data complexity
KW - Medical datasets
KW - Meta-learning
KW - Supervised classifiers
UR - http://www.scopus.com/inward/record.url?scp=85112063493&partnerID=8YFLogxK
U2 - 10.3390/math9151817
DO - 10.3390/math9151817
M3 - Artículo
AN - SCOPUS:85112063493
SN - 2227-7390
VL - 9
JO - Mathematics
JF - Mathematics
IS - 15
M1 - 1817
ER -