Comparing different supervised machine learning algorithms for disease prediction

By S. Uddin, Arif Khan, Md Ekramul Hossain, Mohammed Ali Moni. BMC Medical Informatics and Decision Making, 2019

1,366 citations recorded by the source corpus.

BackgroundSupervised machine learning algorithms have been a dominant method in the data mining field. Disease prediction using health data has recently shown a potential application area for these methods. This study ai7ms to identify the key trends among different types of supervised machine learning algorithms, and their performance and usage for disease risk prediction.MethodsIn this study, extensive research efforts were made to identify those studies that applied more than one supervised machine learning algorithm on single disease prediction. Two databases (i.e., Scopus and PubMed) were searched for different types of search items. Thus, we selected 48 articles in total for the comparison among variants supervised machine learning algorithms for disease prediction.ResultsWe found that the Support Vector Machine (SVM) algorithm is applied most frequently (in 29 studies) followed by the Naïve Bayes algorithm (in 23 studies). However, the Random Forest (RF) algorithm showed superior accuracy comparatively. Of the 17 studies where it was applied, RF showed the highest accuracy in 9 of them, i.e., 53%. This was followed by SVM which topped in 41% of the studies it was considered.ConclusionThis study provides a wide overview of the relative performance of different variants of supervised machine learning algorithms for disease prediction. This important information of relative performance can be used to aid researchers in the selection of an appropriate supervised machine learning algorithm for their studies.

Subfields: Artificial Intelligence, Health Information Management

View Paper: https://www.semanticscholar.org/paper/c8ac6060d34179871b81ecd19621c63360347f8e

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Mohammed Ali Moni

Citations in the corpus

Why do tree-based models still outperform deep learning on tabular data? (2022, positive): “Comparisons between NNs and tree-based models The most comprehensive comparisons of machine learning algorithms have been published before the advent of new deep learning methods, or on speciﬁc problems [Fernández-Delgado et al., 2014, Sakr et al., 2017, Korotcov et al., 2017, Uddin et al., 2019].”
Why do tree-based models still outperform deep learning on typical tabular data? (2022, neutral): “…models The most comprehensive comparisons of machine learning algorithms have been published before the advent of new deep learning methods [Caruana and Niculescu-Mizil, 2006, Fernández-Delgado et al., 2014], or on specific problems [Sakr et al., 2017, Korotcov et al., 2017, Uddin et al., 2019].”
A Review of Feature Selection Methods for Machine Learning-Based Disease Risk Prediction (2022, methodology): “…function-approximation algorithms (e.g., support-vector machine, random forests, K-nearest neighbor, arti ﬁ cial neural network, etc ... ) to create a model that maps the association between a set of risk SNPs and a particular phenotype (Kruppa et al., 2012; Mohri et al., 2018; Uddin et al., 2019).”
Heart disease prediction using supervised machine learning algorithms: Performance analysis and comparison (2021, methodology): “A DT designs the logic of the decision in such a way that evaluates and matches results for the classification of data items into a structure as like a tree [25].”