Improving of Diabetes Diagnosis using Ensembles and Machine Learning Methods
الموضوعات : Majlesi Journal of Telecommunication Devices
Razieh Asgarnezhad
1
,
Karrar Ali Mohsin Alhameedawi
2
1 - Department of Computer Engineering, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran
2 - Department of Computer Engineering, Al-Rafidain University of Baghdad, Baghdad, Ira
الکلمات المفتاحية: Diabetes mellitus, Machine Learning, Ensembles, Pre-processing, Data mining,
ملخص المقالة :
Diabetes is one of the most common metabolic diseases, and diagnosis of it is a classification problem. The most challenge is this area is missing value problem. Artificial Intelligence techniques have been successfully implemented over medical disease diagnoses. Classification systems aim clinicians to predict the risk factors that cause diabetes. To address this challenge, we introduce a novel model to investigate the role of pre-processing and data reduction for classification problems in the diagnosis of diabetes. The model has four stages consists of Pre-processing, Feature sub-selection, Classification, and Performance. In the classification technique, ensemble techniques such as bagging, boosting, stacking, and voting were used. We considered both states with/without for pre-processing stage to reveal the high performance of our model. Two experiments were conducted to reveal the performance of the model for the diagnosis of diabetics Mellitus. The results confirmed the superiority of the proposed method over the state-of-the-art systems, and the best accuracy and F1 achieved 97.12% and 97.40%, respectively.
[1] R. Asgarnezhad and K. Ali Mohsin Alhameedawi, "MVO-Autism: An Effective Pre-treatment with High Performance for Improving Diagnosis of Autism Mellitus," Journal of Electrical and Computer Engineering Innovations (JECEI), 2021. https://doi.org/10.22061/jecei.2021.8109.480
[2] H. F. Ahmad, H. Mukhtar, H. Alaqail, M. Seliaman, and A. Alhumam, "Investigating Health-Related Features and Their Impact on the Prediction of Diabetes Using Machine Learning," Applied Sciences, vol. 11, no. 3, pp. 1173-1189, 2021.
[3] M. Jahangir, H. Afzal, M. Ahmed, K. Khurshid, and R. Nawaz, "An expert system for diabetes prediction using auto tuned multi-layer perceptron," in 2017 Intelligent systems conference (IntelliSys), 2017, pp. 722-728.
[4] R. Asgarnezhad, A. Monadjemi, and M. Soltanaghaei, "NSE-PSO: Toward an Effective Model Using Optimization Algorithm and Sampling Methods for Text Classification," Journal of Electrical and Computer Engineering Innovations (JECEI), vol. 8, pp. 183-192, 2020.
[5] R. Asgarnezhad, S. A. Monadjemi, and M. S. Aghaei, "A new hierarchy framework for feature engineering through multi‐objective evolutionary algorithm in text classification," Concurrency and Computation: Practice and Experience, 2021. https://doi.org/10.1002/cpe.6594
[6] M. M. Nentwich and M. W. Ulbig, "Diabetic retinopathy-ocular complications of diabetes mellitus," World journal of diabetes, vol. 6, no. 3, pp. 489-532, 2015.
[7] T. Daghistani and R. Alshammari, "Diagnosis of diabetes by applying data mining classification techniques," International Journal of Advanced Computer Science and Applications (IJACSA), vol. 7, pp. 329-332, 2016.
[8] R. Asgarnezhad, S. A. Monadjemi, and M. Soltanaghaei, "FAHPBEP: A fuzzy Analytic Hierarchy Process framework in text classification," Majlesi Journal of Electrical Engineering, vol. 14, pp. 111-123, 2020.
[9] D. Sisodia and D. S. Sisodia, "Prediction of diabetes using classification algorithms," Procedia computer science, vol. 132, pp. 1578-1585, 2018.
[10] M. S. Satu, S. T. Atik, and M. A. Moni, "A novel hybrid machine learning model to predict diabetes mellitus," in Proceedings of International Joint Conference on Computational Intelligence, pp. 453-465, 2020.
[11] M. F. Faruque and I. H. Sarker, "Performance analysis of machine learning techniques to predict diabetes mellitus," in 2019 International Conference on Electrical, Computer and Communication Engineering (ECCE), 2019, pp. 1-4.
[12] R. Alshammari, N. Atiyah, T. Daghistani, and A. Alshammari, "Improving Accuracy for Diabetes Mellitus Prediction by Using Deepnet," Online Journal of Public Health Informatics, vol. 12, 2020.
[13] R. T. Selvi and I. Muthulakshmi, "Modelling the map reduce based optimal gradient boosted tree classification algorithm for diabetes mellitus diagnosis system," Journal of Ambient Intelligence and Humanized Computing, vol. 12, pp. 1717-1730, 2021.
[14] R. Asgarnezhad, A. Monadjemi, and M. Soltanaghaei, "A High-Performance Model based on Ensembles for Twitter Sentiment Classification," Journal of Electrical and Computer Engineering Innovations (JECEI), vol. 8, pp. 41-52, 2020.
[15] R. Asgarnezhad, S. A. Monadjemi, and M. Soltanaghaei, "An application of MOGW optimization for feature selection in text classification," The Journal of Supercomputing, vol. 77, pp. 5806-5839, 2021.
[16] R. Asgarnezhad and S. A. Monadjemi, "NB vs. SVM: A contrastive study for sentiment classification on two text domains," Journal of Applied Intelligent Systems & Information Sciences, 2021. https://doi.org/10.22034/JAISIS.2021.279225.1025
