A Multi-Layered Hybrid Machine Learning Algorithm (MLHA) for Type II Diabetes Classification

Machine learning applications in the medical field has proliferated in the past decade. One of the main usages of machine learning in medical field is the detection of diseases from text and image data. In this work, a new hybrid multi-layer algorithm (MLHA) based on machine learning algorithms is p...

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Vydané v:Procedia computer science Ročník 237; s. 445 - 452
Hlavní autori: Jannoud, Ismael, Masoud, Mohammad Z., Jaradat, Yousef, Manaserah, Ahmad, Zaidan, Dema
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier B.V 2024
Predmet:
Diabetes
K-Mean
Machine Learning algorithms
Multi-Layered Hybrid Machine Learning Algorithm (MLHA)
Random Forest
SVM
XGBoost
Multi-Layered Hybrid Machine Learning Algorithm (MLHA)
XGBoost
Diabetes
SVM
K-Mean
Machine Learning algorithms
Random Forest
ISSN:1877-0509, 1877-0509
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Popis
Shrnutí:Machine learning applications in the medical field has proliferated in the past decade. One of the main usages of machine learning in medical field is the detection of diseases from text and image data. In this work, a new hybrid multi-layer algorithm (MLHA) based on machine learning algorithms is proposed to predict diabetes. The algorithm consists of two layers. The first layer consists of three different machine learning algorithms; SVM, random forest and K-mean, that work in parallel on the dataset. The output of this layer is fed into the second layer that consists of only one algorithm; XGBoost, that is trained from the output data of the first layer. We trained the proposed model with two different datasets and its accuracy has been compared with three different common well-known algorithms; feed forward back propagation neural network, logistic regression and random forest. Our results show that the accuracy of MLHA reached 86.5% for the first dataset and approximately 84.6% for the second dataset. This reported accuracy outperformed all the classical models.
ISSN:1877-0509
1877-0509
DOI:10.1016/j.procs.2024.05.126