A DISEASE PREDICTION MODEL USING SPOTTED HYENA SEARCH OPTIMIZATION AND BI-LSTM

Authors

  • AYSHWARYA LAKSHMI S. Department of Computer Science and Engineering, University College of Engineering Panruti, Tamilnadu, India Author

DOI:

https://doi.org/10.59277/RRST-EE.2023.68.1.20

Keywords:

Machine learning, Deep learning, Optimization, Spotted hyena optimization algorithm, Cuckoo search optimization algorithm, Bidirectional long short-term memory, Prediction, classification, and fuzzy temporal

Abstract

Early disease prediction is the best solution for any deadly disease in this fast internet world. In this direction, machine learning and deep learning techniques are applied in this fast world to predict diseases early and update the disease level through the Internet of Things techniques. For this purpose, this paper proposes a disease prediction system that uses the newly proposed fuzzy temporal correlation aware classifier and the auto-encoded bidirectional long short-term memory (FTC-Bi-LSTM) to predict the diseases such as heart, cancer, and diabetes. Moreover, this paper proposes a hybrid optimization algorithm called spotted hyena and cuckoo search optimization algorithm (SHCSA) to select the contributed features used to enrich the prediction accuracy. The standard benchmark UCI repository dataset is used to conduct the various experiments and obtain better performance in terms of precision, recall, f-measure, and prediction accuracy. The result is validated by considering the hospital patient data as input and proving the performance.

References

(1) R. Joshi, M. Alehegn, Analysis and prediction of diabetes diseases using machine learning algorithm: Ensemble approach, Int. Res. J Engg. Tech., 4, 10, pp. 426–435 (2017).

(2) N.J.F. Ndisang, A. Vannacci, S. Rastogi, Insulin Resistance, Type 1 and Type 2 Diabetes, and Related Complications, J Diab. Res., 2017, id:1478294 (2017).

(3) H.P. Himsworth and R. B. Kerr, Insulin-sensitive and Insulin-insensitive types of diabetes mellitus, Clinical Sciences, 4, pp. 119–152 (1939).

(4) R. Sethukkarasi, S. Ganapathy, P. Yogesh, A. Kannan, An intelligent neuro fuzzy temporal knowledge representation model for mining temporal patterns, Journal of Intelligent & Fuzzy Systems, 26, 3, pp. 1167–1178 (2014).

(5) Y. Hayashi, S. Yukita, Rule extraction using Recursive-Rule extraction algorithm with J48 graft combined with sampling selection techniques for the diagnosis of type 2 diabetes mellitus in the Pima Indian dataset, Informatics in Medicine Unlocked, 2, pp. 92–104 (2016).

(6) H. Kazemian, S.A. Yusuf, K. White, C.M. Grimaldi, NN approach and its comparison with NN-SVM to beta-barrel prediction, Expert Systems with Applications, 61, pp. 203–214 (2016).

(7) I. Kavakiotis, O. Tsave, A. Salifoglou, N. Maglaveras, I. Vlahavas et al., Machine Learning and Data Mining Methods in Diabetes Research, Computational and Structural Biotechnology Journal, 15, pp. 104–116 (2017).

(8) F. Mansourypoor, S. Asadi, Development of a Reinforcement Learning-based Evolutionary Fuzzy Rule-Based System for diabetes diagnosis, Computers in Biology and Medicine, 91, pp. 337–352 (2017).

(9) A. Talaei-Khoei, J.M. Wilson, Identifying people at risk of developing type 2 diabetes: A comparison of predictive analytics techniques and predictor variables, International Journal of Medical Informatics, 119, pp. 22–38 (2018).

(10) N. Singh, P. Singh, D. Bhagat, A rule extraction approach from support vector machines for diagnosing hypertension among diabetics, Expert Systems with Applications, 130, pp. 188–205 (2019).

(11) M. Alirezaei, S.d Taghi, A. Niaki, S. Armin, A. Niaki, A bi-objective hybrid optimization algorithm to reduce noise and data dimension in diabetes diagnosis using support vector machines, Expert Systems with Applications, 127, pp. 47–57 (2019).

(12) U. Kanimozhi, S. Ganapathy, D. Manjula, A. Kannan, An intelligent risk prediction system for breast cancer using fuzzy temporal rules, National Academy Science and Letters,.42, 3, pp. 227–232 (2019).

(13) R.K. Bania, A. Halder, R-Ensembler, A greedy rough set based ensemble attribute selection algorithm with kNN imputation for classification of medical data, Computer Methods and Programs in Biomedicine, 184, 105122 (2020).

(14) N. Singh, P. Singh, Stacking-based multi-objective evolutionary ensemble framework for prediction of diabetes mellitus, Biocybernetics and Biomedical Engineering, 40, 1, pp. 1–22, (2020).

(15) M.T. García-Ordás, C. Benavides, J.A. Benítez-Andrades, H.A. Moretón, I. García-Rodríguez, Diabetes detection using deep learning techniques with oversampling and feature augmentation, Computer Methods and Programs in Biomedicine, 202, Article ID: 105968, (2021).

(16) X. Xu, M. Yoneda, Multitask air-quality prediction based on LSTM-autoencoder model, IEEE Transactions on Cybernetics, 51, 5, pp. 2577–2586 (2021).

(17) P.M. Murphy, D.W. Aha, UCI Repository of machine learning databases (machine-readable data repository, University of California, Dept. Inf. Comput. Sci., Irvine, CA (1993).

(18) S. Ganapathy, R. Sethukkarasi, P. Yogesh, P. Vijayakumar, A. Kannan, An intelligent temporal pattern classification system using fuzzy temporal rules and particle swarm optimization, Sadhana, 39, 2, pp. 283–302, (2014).

(19) R.P. Cherian, N. Thomas, S. Venkitachalam, Weight optimized neural network for heart disease prediction using hybrid lion plus particle swarm algorithm, Journal of Biomedical Informatics, 110, Article no. 103543, pp. 1–11 (2020).

(20) R. Rajabioun, Cuckoo optimization algorithm, Applied Soft Computing, 11, 8, pp. 5508–5518 (2011).

(21) H.T Gupta, P. Kumar, S. Saurabh, S.K. Mishra, B. Appasani, A. Pati, C. Ravariu, A. Srinivasulu, Category boosting machine learning algorithm for breast cancer prediction, Rev. Roum. Sci. Techn.– Électrotechn. et Énerg., 66, 3, pp. 201–206, Bucarest (2021).

(22) M.I. Abdelwanis, R. El-Sehiemy, M.A. Hamida, Parameter estimation of permanent magnet synchronous machines using particle swarm optimization algorithm, Rev. Roum. Sci. Techn.–Électrotechn. et Énerg., 67, 4, pp. 377–382, Bucarest (2022).

(23) V.E. Jesi, S.M. Aslam, An intelligent disease prediction and monitoring system using feature selection, multi-neural network and fuzzy rules, Neural Computing and Applications, 34, 22, pp. 19877–19893 (2022)

Downloads

Published

01.04.2023

Issue

Section

Génie biomédical | Biomedical Engineering

How to Cite

A DISEASE PREDICTION MODEL USING SPOTTED HYENA SEARCH OPTIMIZATION AND BI-LSTM. (2023). REVUE ROUMAINE DES SCIENCES TECHNIQUES — SÉRIE ÉLECTROTECHNIQUE ET ÉNERGÉTIQUE, 68(1), 113-118. https://doi.org/10.59277/RRST-EE.2023.68.1.20