Main Article Content
Diabetes is a seriously deadly disease today. It is necessary to enable patients to control their blood glucose levels. Even though, in the past, various researchers proposed numerous diabetic detection and prediction systems they are not fulfilling the requirements in terms of detection and prediction accuracy. Nowadays, diabetes patients are utilizing the gadgets like Wireless Insulin Pump that passes into the body instead of syringes for filling insulin. Within this context, insulin treatment is necessary for avoiding life-threatening. Toward this mission, a new deep learning approach-based disease detection system is introduced which takes care of identifying Type-1 and Type-2 diabetes, heart diseases, and breast cancer. In this system, a new Conditional Random Field based Butterfly Optimization Algorithm (CRF-BOA) is developedto select the important features for identifying the Type-1 and Type-2 diabetic disease. Besides, a new fuzzy ID3 classification method is developed for classifying the patient's datasets either normal or abnormal and disease affected. Ultimately, by applying the deep belief network (DBN) the classified patient records are involved with training to identify the relevant symptoms of similarity and glucose status of various patient records. These experiments are being conducted for proving the efficiency of the proposed deep learning approach in terms of glucose monitoring efficiency and disease prediction accuracy.The proposed approach achieved high detection accuracy than the current deep learning approaches in this directionbased on error rate and accuracy.
MainakBiswas, VenkatanareshbabuKuppili, Damodar Reddy Edla, Harman S. Suri, Luca Saba, RuiTatoMarinhoe, J. Miguel Sanches, Jasjit S. Suri, "Symtosis: A liver ultrasound tissue characterization and risk stratification in optimized deep learning paradigm", Computer Methods and Programs in Biomedicine, Vol.155, pp. 165-177, 2018.
NoureenTalpur, Said JadidAbdulkadir, HithamAlhussian, MohdHilmiHasan, MohdHafizulAfifi Abdullah, "Optimizing deep neuro-fuzzy classifier with a novel evolutionary arithmetic optimization algorithm", Journal of Computational Science, Vol.64, No.101867, 2022.
Marco Pota, Massimo Esposito, Giuseppe De Pietro, "Designing rule-based fuzzy systems for classification in medicine", Knowledge-Based Systems, Vol.124, pp. 105-132, 2017.
IlhemBoussaïd, JulienLepagnot, Patrick Siarry, "A survey on optimization metaheuristics", Information Sciences, Vol.237, pp. 82-117, 2013.
HosseinAhmadi, MarsaGholamzadeh, Leila Shahmoradi, MehrbakhshNilashi, PooriaRashvand, "Diseases diagnosis using fuzzy logic methods: A systematic and meta-analysis review", Computer Methods and Programs in Biomedicine, Vol.161, pp. 145-172, 2018.
P. Ganesh Kumar, T. Aruldoss Albert Victoire, P. Renukadevi, D. Devaraj, "Design of fuzzy expert system for microarray data classification using a novel Genetic Swarm Algorithm", Expert Systems with Applications, Vol.39, No.2, pp. 1811-1821, 2012.
SoumadipGhosh, SushantaBiswas, DebasreeSarkar, ParthaPratimSarkar, "A novel Neuro-fuzzy classification technique for data mining", Egyptian Informatics Journal, Vol.15, No.3, pp. 129-147, 2014.
U. Ahmed et al., "Prediction of Diabetes Empowered With Fused Machine Learning", IEEE Access, vol. 10, pp. 8529-8538, 2022.
S. Mohan, C. Thirumalai and G. Srivastava, "Effective Heart Disease Prediction Using Hybrid Machine Learning Techniques", IEEE Access, vol. 7, pp. 81542-81554, 2019.
J. P. Li, A. U. Haq, S. U. Din, J. Khan, A. Khan and A. Saboor, "Heart Disease Identification Method Using Machine Learning Classification in E-Healthcare", IEEE Access, vol. 8, pp. 107562-107582, 2020.
S. Zheng et al., "Multi-Modal Graph Learning for Disease Prediction", IEEE Transactions on Medical Imaging, vol. 41, no. 9, pp. 2207-2216, Sept. 2022.
N. L. Fitriyani, M. Syafrudin, G. Alfian and J. Rhee, "HDPM: An Effective Heart Disease Prediction Model for a Clinical Decision Support System", IEEE Access, vol. 8, pp. 133034-133050, 2020.
J. Xie and Q. Wang, "Benchmarking Machine Learning Algorithms on Blood Glucose Prediction for Type I Diabetes in Comparison with Classical Time-Series Models", IEEE Transactions on Biomedical Engineering, vol. 67, no. 11, pp. 3101-3124, Nov. 2020.
D. Bertsimas, L. Mingardi and B. Stellato, "Machine Learning for Real-Time Heart Disease Prediction", IEEE Journal of Biomedical and Health Informatics, vol. 25, no. 9, pp. 3627-3637, Sept. 2021.
Perez-Siguas, R. ., Matta-Solis, H. ., Matta-Solis, E. ., Matta-Perez, H. ., Cruzata-Martinez, A. . and Meneses-Claudio, B. . (2023) “Management of an Automatic System to Generate Reports on the Attendance Control of Teachers in a Educational Center”, International Journal on Recent and Innovation Trends in Computing and Communication, 11(2), pp. 20–26. doi: 10.17762/ijritcc.v11i2.6106.
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, Springer, Vol. 39, No.2, pp. 283-302, 2014.
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, IOS Press, Vol. 26, No.3, pp. 1167-1178, 2014.
U.Kanimozhi, S.Ganapathy, D.Manjula, A.Kannan, "An Intelligent Risk Prediction System for Breast Cancer Using Fuzzy Temporal Rules", National Academic Science Letters, Vol.42, No.3, pp. 227-232, 2019.
Meneses-Claudio, B. ., Perez-Siguas, R. ., Matta-Solis, H. ., Matta-Solis, E. ., Matta-Perez, H. ., Cruzata-Martinez, A. ., Saberbein-Muñoz, J. . and Salinas-Cruz, M. . (2023) “Automatic System for Detecting Pathologies in the Respiratory System for the Care of Patients with Bronchial Asthma Visualized by Computerized Radiography”, International Journal on Recent and Innovation Trends in Computing and Communication, 11(2), pp. 27–34. doi: 10.17762/ijritcc.v11i2.6107.
Jothi CS, Ravikumar S, Kumar A, Suresh A. An approach for verifying correctness of web service compositions. International Journal of Engineering &Technology. 2018;7(1.7):5-10.
Ravikumar, S. and Kannan, E., 2022. Machine Learning Techniques for Identifying Fetal Risk During Pregnancy. International Journal of Image and Graphics, 22(05), p.2250045.
Ravikumar, S. and Kannan, E., 2022. Analysis on Mental Stress of Professionals and Pregnant Women Using Machine Learning Techniques. International Journal of Image and Graphics, p.2350038.
MJ, C.M.B., Arif, M. and V, D.K., 2022. Linguistic Analysis of Hindi-English Mixed Tweets for Depression Detection. Journal of Mathematics, 2022, pp.1-7.
Kannan, E., Ravikumar, S., Anitha, A., Kumar, S.A. and Vijayasarathy, M., 2021. Analyzing uncertainty in cardiotocogram data for the prediction of fetal risks based on machine learning techniques using rough set. Journal of Ambient Intelligence and Humanized Computing, pp.1-13.
Antony Kumar, K. and Carmel Mary Belinda, M.J., 2022. A Multi-Layer Acoustic Neural Network-Based Intelligent Early Diagnosis System for Rheumatic Heart Disease. International Journal of Image and Graphics, (p.2450012).