A Diabetes Diagnosis Model using Optimized Long Short-Term Memory Based on Improved Particle Swarm Optimization

Revathy J; Jayanthi S.K

doi:10.54392/irjmt2514

Authors

Revathy J Department of Computer Science, Vellalar College for Women, Erode-638012, Tamil Nadu, India. Author https://orcid.org/0009-0000-4266-310X
Jayanthi S.K Department of Computer Science, Vellalar College for Women, Erode-638012, Tamil Nadu, India. Author

DOI:

https://doi.org/10.54392/irjmt2514

Keywords:

Long short-term memory, Hyper parameter optimization, Particle swarm optimization, Partial opposition-based learning, Diabetic prediction, Local search algorithm

Abstract

Diabetes, a chronic disease, arises when the body is either unable to utilize the insulin generated by the pancreas or is unable to create enough of it. It could be the deadliest if left undiagnosed and untreated. If diabetes is identified early enough, a person can receive the right care and live a healthy life. An automated system is required to identify diabetes from clinical and physical data when the traditional method is laborious. The paper proposed a new diabetes classifying model based on optimized long short-term memory (LSTM). The proposed method uses a new variant of particle swarm optimization (PSO) based on partial opposition-based learning (POBL) and a local search algorithm (LSA) approach called PLPSO for optimizing hyperparameters of LSTM (PLPSO-LSTM). PSO uses the POBL during the initialization phase to increase population diversity and the LSA during the updating position to increase exploitation. The proposed model has been tested using four diabetes datasets for analyzing its performance. These results show that optimized PLPSO-LSTM performs better than other state-of-the-art algorithms.

References

W.H.O. WHO. (2023) Diabetes. WHO, India. https://www.who.int/india/health-topics/mobile-technology-for-preventing-ncds#:~:text=In%20India%2C%20there%20are%20estimated,developing%20diabetes%20in%20near%20future

K.M. West, M.M.S. Ahuja, P.H. Bennett, A. Czyzyk, O.M. De Acosta, J.H. Fuller, B. Grab, V. Grabauskas,R.J. Jarrett, K. Kosaka, H. Keen, A.S. Krolewski, E. Miki, V. Schliack, A. Teuscher, P.J. Watkins, J.A. Stober, The role of circulating glucose and triglyceride concentrations and their interactions with other “risk factors” as determinants of arterial disease in nine diabetic population samples from the WHO multinational study. Diabetes care, 6(4), (1983) 361-369. https://doi.org/10.2337/diacare.6.4.361

N. Sneha, T. Gangil, Analysis of diabetes mellitus for early prediction using optimal features selection. Journal of Big data, 6(1), (2019) 1-19. https://doi.org/10.1186/s40537-019-0175-6

R. Kamalraj, S. Neelakandan, M. Ranjith Kumar, V. Chandra Shekhar Rao, R. Anand, H. Singh, Interpretable filter based convolutional neural network (IF-CNN) for glucose prediction and classification using PD-SS algorithm. Measurement, 183, (2021) 109804. https://doi.org/10.1016/j.measurement.2021.109804

B.F. Wee, S. Sivakumar, K.H. Lim, W. Wong, F.H. Juwono, Diabetes detection based on machine learning and deep learning approaches. Multimedia Tools and Applications, 83(8), (2024) 24153-24185. https://doi.org/10.1007/s11042-023-16407-5

R. Annamalai, R. Nedunchelian, Design of optimal bidirectional long short term memory based predictive analysis and severity estimation model for diabetes mellitus. International Journal of Information Technology, 15(1), (2023) 447-455. https://doi.org/10.1007/s41870-022-00933-w

A. ElSaid, F. El Jamiy, J. Higgins, B. Wild, T. Desell, Optimizing long short-term memory recurrent neural networks using ant colony optimization to predict turbine engine vibration. Applied Soft Computing, 73, (2018) 969-991. https://doi.org/10.1016/j.asoc.2018.09.013

H. Chung, K.S. Shin, Genetic algorithm-optimized long short-term memory network for stock market prediction. Sustainability, 10(10), (2018) 3765. https://doi.org/10.3390/su10103765

P. Pirozmand, H. Jalalinejad, A. A. R. Hosseinabadi, S. Mirkamali, Y. Li, An improved particle swarm optimization algorithm for task scheduling in cloud computing. Journal of Ambient Intelligence and Humanized Computing, 14(4), (2023) 4313-4327. https://doi.org/10.1007/s12652-023-04541-9

H.R. Tizhoosh, (2005) Opposition-based learning: a new scheme for machine intelligence. International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC'06), IEEE, Austria. https://doi.org/10.1109/CIMCA.2005.1631345

W.H. Bangyal, K. Nisar, T.R. Soomro, A.A. Ag Ibrahim, G.A. Mallah, N.U. Hassan, N.U. Rehman, An improved particle swarm optimization algorithm for data classification. Applied Sciences, 13(1), (2022) 283. https://doi.org/10.3390/app13010283

J. Xu, S. Xu, L. Zhang, C. Zhou, Z. Han, A particle swarm optimization algorithm based on diversity-driven fusion of opposing phase selection strategies. Complex & Intelligent Systems, 9(6), (2023) 6611-6643. https://doi.org/10.1007/s40747-023-01069-5

J.P. Kandhasamy, S. Balamurali, Performance analysis of classifier models to predict diabetes mellitus. Procedia Computer Science, 47, (2015) 45-51. https://doi.org/10.1016/j.procs.2015.03.182

O. Erkaymaz, M. Ozer, Impact of small-world network topology on the conventional artificial neural network for the diagnosis of diabetes. Chaos, Solitons & Fractals, 83, (2016) 178-185. https://doi.org/10.1016/j.chaos.2015.11.029

S. Yu, Z. Cao, X. Jiang, (2017) Robust linear discriminant analysis with a Laplacian assumption on projection distribution. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, USA. https://doi.org/10.1109/ICASSP.2017.7952620

W. Chen, S. Chen, H. Zhang, T. Wu, (2017) A hybrid prediction model for type 2 diabetes using K-means and decision tree. IEEE international conference on software engineering and service science (ICSESS), IEEE, China. https://doi.org/10.1109/ICSESS.2017.8342938

S. M. H. Dadgar, M. Kaardaan, A hybrid method of feature selection and neural network with genetic algorithm to predict diabetes. International Journal of Mechatronics, Electrical and Computer Technology (IJMEC), 7, (2017) 3397-3404.

E. Hashi, S.U. Zaman, R. Hasan, Developing diabetes disease classification model using sequential forward selection algorithm. International Journal of Computer Applications, 180(5), (2017)1-6. https://doi.org/10.5120/ijca2017916018

Y. Zhang, Z. Lin, Y. Kang, R. Ning, Y. Meng, A feed-forward neural network model for the accurate prediction of diabetes mellitus. International Journal of Scientific and Technology Research, 7(8), (2018) 151-155.

R. Haritha, D. S. Babu, P. Sammulal, A hybrid approach for prediction of type-1 and type-2 diabetes using firefly and cuckoo search algorithms. International Journal of Applied Engineering Research, 13(2), (2018) 896-907.

S. Srivastava, L. Sharma, V. Sharma, A. Kumar, H. Darbari, Prediction of diabetes using artificial neural network approach. Engineering Vibration, Communication and Information Processing: ICoEVCI 2018, Springer, 679-687. https://doi.org/10.1007/978-981-13-1642-5_59

K. Kannadasan, D. R. Edla, V. Kuppili, Type 2 diabetes data classification using stacked autoencoders in deep neural networks. Clinical Epidemiology and Global Health, 7(4), (2019) 530-535. https://doi.org/10.1016/j.cegh.2018.12.004

N.S. Prema, V. Varshith, J. Yogeswar, Prediction of diabetes using ensemble techniques. International Journal of Recent Technology and Engineering, 7(6), (2019) 203-205.

A. Dinh, S. Miertschin, A. Young, and S. D. Mohanty, A data-driven approach to predicting diabetes and cardiovascular disease with machine learning. BMC medical informatics and decision making, 19(1), (2019) 1-15. https://doi.org/10.1186/s12911-019-0918-5

N. Pradhan, G. Rani, V. S. Dhaka, R. C. Poonia, Diabetes prediction using artificial neural network. Deep Learning Techniques for Biomedical and Health Informatics, (2020) 327-339. https://doi.org/10.1016/B978-0-12-819061-6.00014-8

K. Lakhwani, S. Bhargava, K. K. Hiran, M. M. Bundele, D. Somwanshi, (2020) Prediction of the onset of diabetes using artificial neural network and Pima Indians diabetes dataset. 5th IEEE International Conference on Recent Advances and Innovations in Engineering (ICRAIE), IEEE, India. https://doi.org/10.1109/ICRAIE51050.2020.9358308

M. Abedini, A. Bijari, T. Banirostam, Classification of Pima Indian diabetes dataset using ensemble of decision tree, logistic regression and neural network. International Journal of Advanced Research in Computer and Communication Engineering, 9(7), (2020) 7-10. https://doi.org/10.17148/IJARCCE.2020.9701

H. Naz, S. Ahuja, Deep learning approach for diabetes prediction using PIMA Indian dataset. Journal of Diabetes & Metabolic Disorders, 19, (2020) 391-403. https://doi.org/10.1007/s40200-020-00520-5

N. Pradhan, V.S. Dhaka, S.C. Kulhari, Experimental and comparison based study on diabetes prediction using artificial neural network. Recent Advances in Computer Science and Communications (Formerly: Recent Patents on Computer Science), 13(6), (2020) 1173-1179. https://doi.org/10.2174/2213275912666190801112119

E. Güldoğan, T. Zeynep, A. Ayça, C. Colak, Performance evaluation of different artificial neural network models in the classification of type 2 diabetes mellitus. The Journal of Cognitive Systems, 5(1), (2020) 23-32.

M.M. Bukhari, B.F. Alkhamees, S. Hussain, A. Gumaei, A. Assiri, S.S. Ullah, An improved artificial neural network model for effective diabetes prediction. Complexity, 2021(1), (2021) 5525271. https://doi.org/10.1155/2021/5525271

M.W. Nadeem, H.G. Goh, V. Ponnusamy, I. Andonovic, M.A. Khan, M. Hussain, A fusion-based machine learning approach for the prediction of the onset of diabetes. In Healthcare, 9(10), (2021) 1393. https://doi.org/10.3390/healthcare9101393

P.B.K. Chowdary, R.U. Kumar, An effective approach for detecting diabetes using deep learning techniques based on convolutional LSTM networks. International Journal of Advanced Computer Science and Applications, 12(4), (2021) 519-525. https://dx.doi.org/10.14569/IJACSA.2021.0120466

S. Sivasankari, J. Surendiran, N. Yuvaraj, M. Ramkumar, C. Ravi, R. Vidhya, (2022) Classification of diabetes using multilayer perceptron. IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE, India. https://doi.org/10.1109/ICDCECE53908.2022.9793085

P. Pujari, Classification of Pima Indian diabetes dataset using support vector machine with polynomial kernel. Deep Learning, Machine Learning and IoT in Biomedical and Health Informatics: CRC Press, (2022) 55-67. https://doi.org/10.1201/9780367548445-5

V. Chang, J. Bailey, Q. A. Xu, Z. Sun, Pima Indians diabetes mellitus classification based on machine learning (ML) algorithms. Neural Computing and Applications, 35(22), (2023) 16157-16173. https://doi.org/10.1007/s00521-022-07049-z

A. Mousa, W. Mustafa, R.B. Marqas, S.H. Mohammed, A comparative study of diabetes detection using the Pima Indian diabetes database. Journal of Duhok University, 26(2), (2023) 277-288. https://doi.org/10.26682/sjuod.2023.26.2.24

H. Shao, X. Liu, D. Zong, Q. Song, Optimization of diabetes prediction methods based on combinatorial balancing algorithm. Nutrition & Diabetes, 14(1), (2024) 63. https://doi.org/10.1038/s41387-024-00324-z

M. Rahman, D. Islam, R.J. Mukti, I. Saha, A deep learning approach based on convolutional LSTM for detecting diabetes. Computational Biology and Chemistry, 88, (2020) 107329. https://doi.org/10.1016/j.compbiolchem.2020.107329

S. Hochreiter, J. Schmidhuber, Long short-term memory. Neural computation, MIT Press, 9(8), (1997) 1735-1780. https://doi.org/10.1162/neco.1997.9.8.1735

Z. Hu, Y. Bao, T. Xiong, (2014) Partial opposition-based adaptive differential evolution algorithms: Evaluation on the CEC 2014 benchmark set for real-parameter optimization. IEEE Congress on Evolutionary Computation (CEC), IEEE, China. https://doi.org/10.1109/CEC.2014.6900489

M. Tubishat, N. Idris, L. Shuib, M.A. Abushariah, S. Mirjalili, Improved Salp Swarm Algorithm based on opposition based learning and novel local search algorithm for feature selection. Expert Systems with Applications, 145, (2020) 113122. https://doi.org/10.1016/j.eswa.2019.113122

S. Joshi, M. Borse, (2016) Detection and prediction of diabetes mellitus using back-propagation neural network. in 2016 International conference on micro-electronics and telecommunication engineering (ICMETE), IEEE, India. https://doi.org/10.1109/ICMETE.2016.11

M.K. Suddle, M. Bashir, Metaheuristics based long short term memory optimization for sentiment analysis. Applied Soft Computing, 131, (2022) 109794. https://doi.org/10.1016/j.asoc.2022.109794

S. Srivastava, (2021) Genetic Algorithm Optimized Deep Learning Model for Parkinson Disease Severity Detection. Dublin, National College of Ireland.

Y. Zhang, S. Yang, (2019) Prediction on the highest price of the stock based on PSO-LSTM neural network. 3rd International Conference on Electronic Information Technology and Computer Engineering (EITCE), IEEE, China. https://doi.org/10.1109/EITCE47263.2019.9094982

H. Zhou, R. Myrzashova, R. Zheng, Diabetes prediction model based on an enhanced deep neural network. EURASIP Journal on Wireless Communications and Networking, 2020, (2020) 1-13. https://doi.org/10.1186/s13638-020-01765-7