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An Improved Homogeneous Ensemble Technique for Early Accurate Detection of Type 2 Diabetes Mellitus (T2DM)

School of Computer Science and Engineering, Central South University, Changsha 410083, China
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Academic Editor: Demos T. Tsahalis
Computation 2022, 10(7), 104; https://doi.org/10.3390/computation10070104
Received: 16 March 2022 / Revised: 20 May 2022 / Accepted: 10 June 2022 / Published: 23 June 2022
The objective of the present study is to improve the genetic algorithm (GA) supremacy in selecting the most suitable and relevant features within a highly dimensional dataset. This results in cost reduction and improving classification performance. During text classification, employing terms such as features using vector space representation can result in a high dimensionality of future space. This condition presents some issues, including high computation cost in data analysis and deteriorating classification accuracy performance. Several computational feature selection techniques can be applied in eliminating the least significant features within a dataset, including a genetic algorithm. The present study improved the performance of the classifier in classifying Pima Indian diabetes data. Despite the popularity of GA in the feature selection area, it does not provide the most optimal features due to one of its underlying issues: premature convergence due to insufficient population diversity in the future generations. GA was improved in its crossover operator using two steps: define a variable slice point on the size of the gene to be interchanged for every offspring generation and apply feature frequency scores in deciding the interchanging of genes. The above obtained results to the proposed technique will be better results than the results for standard GA. Our proposed algorithm attained an accuracy of 97.5%, precision of 98, recall of 97% and F1-score of 97%. View Full-Text
Keywords: type 2 diabetes mellitus; machine learning; homogenous ensemble; decision tree; genetic algorithm; Pima Indian Diabetes Dataset type 2 diabetes mellitus; machine learning; homogenous ensemble; decision tree; genetic algorithm; Pima Indian Diabetes Dataset
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MDPI and ACS Style

Faustin, U.M.; Zou, B. An Improved Homogeneous Ensemble Technique for Early Accurate Detection of Type 2 Diabetes Mellitus (T2DM). Computation 2022, 10, 104. https://doi.org/10.3390/computation10070104

AMA Style

Faustin UM, Zou B. An Improved Homogeneous Ensemble Technique for Early Accurate Detection of Type 2 Diabetes Mellitus (T2DM). Computation. 2022; 10(7):104. https://doi.org/10.3390/computation10070104

Chicago/Turabian Style

Faustin, Umuhire M., and Beiji Zou. 2022. "An Improved Homogeneous Ensemble Technique for Early Accurate Detection of Type 2 Diabetes Mellitus (T2DM)" Computation 10, no. 7: 104. https://doi.org/10.3390/computation10070104

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