NOVEL MULTI-MODAL OBSTRUCTION MODULE FOR DIABETES MELLITUS CLASSIFICATION USING EXPLAINABLE MACHINE LEARNING
Reehana SHAIK
skreehana.15@gmail.comVIT-AP UNIVERSITY (India)
https://orcid.org/0000-0002-2189-3616
Ibrahim SIDDIQUE
VIT AP UNIVERSITY (India)
https://orcid.org/0000-0003-3310-6090
Abstract
Diabetes Mellitus (DM) is a persistent metabolic disorder which is characterized by increased blood glucose level in the blood stream. Initially, DM occurs while the insulin secretion in the pancreas has a disability to secrete or to use hormone for the metabolic process. Moreover, there are different types of DM depending on the physiological process, and the types include Type1 DM, Type2 DM and Gestational DM. Electrocardiography (ECG) waves are used to detect the abnormal heartbeats and cannot directly detect DM, but the wave abnormality can indicate the possibility and presence of DM. Whereas the Photoplethysmography (PPG) signals are a non-invasive method used to detect changes in blood volume that can monitor BG changes. Furthermore, the detection and classification of DM using PPG and ECG can involve analyzing the functional performance of these modalities. By extracting the features like R wave (W1) and QRS complex (W2) in the ECG signals and Pulse Width (S1) and Pulse Amplitude Variation (S2) can detect DM and can be classified into DM and Non-DM. The authors propose a Novel architecture in the basis of Encoder Decoder structure named as Obstructive Encoder Decoder module. This module extracts the specific features and the proposed novel Obstructive Erasing Module remove the remaining artifacts and then the extracted features are fed into the Multi-Uni-Net for the fusion of the two modalities and the fused image is classified using EXplainable Machine Learning (EX-ML). From this classification the performance metrics like Accuracy, Precision, Recall, F1-Score and AUC can be determined.
Keywords:
Diabetes Mellitus, Electrocardiogram (ECG), Non-Invasive method, Photoplethysmography, Feature Extraction, Explainable MLReferences
Ahamed, A. K. A., Lalitha, K., Saravanan, S., & Muthu Kumar, S. (2023). Enhanced Deep Learning based non-invasive anomaly detection of ECG signals with emphasis on diabetes. International Journal of Intelligent Systems and Applications in Engineering, 11(6s), 284-294.
Google Scholar
Cordeiro, R., Karimian, N., & Park, Y. (2021). Hyperglycemia identification using ECG in Deep Learning era. Sensors, 21(18), 6263. https://doi.org/10.3390/s21186263
Google Scholar
Dave, D., Vyas, K., Branan, K., McKay, S., DeSalvo, D. J., Gutierrez-Osuna, R., Cote, G. L., & Erraguntla, M. (2024). Detection of hypoglycemia and hyperglycemia using noninvasive wearable sensors: Electrocardiograms and accelerometry. Journal of Diabetes Science and Technology, 18(2), 351–362. https://doi.org/10.1177/19322968221116393
Google Scholar
Gupta, S., Singh, A., Sharma, A., & Tripathy, R. K. (2022). dSVRI: A PPG-based novel feature for early diagnosis of type-II diabetes mellitus. IEEE Sensors Letters, 6(9), 1-4. https://doi.org/10.1109/LSENS.2022.3203609
Google Scholar
Hina, A., & Saadeh, W. (2022). A 186μW photoplethysmography-based noninvasive glucose sensing SoC. IEEE Sensors Journal, 22(14), 14185-14195. https://doi.org/10.1109/JSEN.2022.3180893
Google Scholar
Jain, A., Verma, A., & Verma, A. K. (2023). Non-invasive and automatic identification of diabetes using ECG signals. International Journal of Electrical and Electronics Research, 11(2), 418-425. https://doi.org/10.37391/ijeer.110223
Google Scholar
Khan, M., Kumar Singh, B., & Nirala, N. (2023). Expert diagnostic system for detection of hypertension and diabetes mellitus using discrete wavelet decomposition of photoplethysmogram signal and machine learning technique. Medicine in Novel Technology and Devices, 19, 100251. https://doi.org/10.1016/j.medntd.2023.100251
Google Scholar
Kulkarni, A. R., Patel, A. A., Pipal, K. V., Jaiswal, S. G., Jaisinghani, M. T., Thulkar, V., Gajbhiye, L., Gondane, P., Patel, A. B., Mamtani, M., & Kulkarni, H. (2023). Machine-Learning algorithm to non-invasively detect diabetes and pre-diabetes from electrocardiogram. BMJ Innovations, 9(1), 32-42. https://doi.org/10.1136/bmjinnov-2021-000759
Google Scholar
Lee, P.-L., Wang, K.-W., & Hsiao, C.-Y. (2023). A noninvasive blood glucose estimation system using dual-channel PPGs and pulse-arrival velocity. IEEE Sensors Journal, 23(19), 23570-23582. https://doi.org/10.1109/JSEN.2023.3306343
Google Scholar
Li, J., Ma, J., Omisore, O. M., Liu, Y., Tang, H., Ao, P., Yan, Y., Wang, L., & Nie, Z. (2024). Noninvasive blood glucose monitoring using spatiotemporal ECG and PPG feature fusion and weight-based choquet Integral multimodel approach. IEEE Transactions on Neural Networks and Learning Systems, 35(10), 14491-14505. https://doi.org/10.1109/TNNLS.2023.3279383
Google Scholar
Li, J., Tobore, I., Liu, Y., Kandwal, A., Wang, L., & Nie, Z. (2021). Non-invasive monitoring of three glucose ranges based on ECG by using DBSCAN-CNN. IEEE Journal of Biomedical and Health Informatics, 25(9), 3340-3350. https://doi.org/10.1109/JBHI.2021.3072628
Google Scholar
Mishra, B., & Nirala, N. (2023). Type2 diabetes classification from short photoplethysmogram signal using multiple domain features and Machine Learning techniques. Research on Biomedical Engineering, 39(3), 541-560. https://doi.org/10.1007/s42600-023-00287-7
Google Scholar
Mishra, B., Nirala, N., & Singh, B. K. (2024). Type-2 diabetes identification from toe-photoplethysmography using Fourier decomposition method. Neural Computing and Applications, 36(5), 2429-2443. https://doi.org/10.1007/s00521-023-09208-2
Google Scholar
Navaneethakrishna, M., & Manuskandan, S. R. (2021). Analysis of heart rate variability in normal and diabetic ECG signals using fragmentation approach. 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (pp. 1112-1115). IEEE. https://doi.org/10.1109/EMBC46164.2021.9631076
Google Scholar
Pal, P., & Mahadevappa, M. (2023). Adaptive multidimensional dual attentive DCNN for detecting cardiac morbidities using fused ECG-PPG signals. IEEE Transactions on Artificial Intelligence, 4(5), 1225-1235. https://doi.org/10.1109/TAI.2022.3184656
Google Scholar
Prabha, A., Yadav, J., Rani, A., & Singh, V. (2021). Design of intelligent diabetes mellitus detection system using hybrid feature selection based XGBoost classifier. Computers in Biology and Medicine, 136, 104664. https://doi.org/10.1016/j.compbiomed.2021.104664
Google Scholar
Prabha, A., Yadav, J., Rani, A., & Singh, V. (2022). Intelligent estimation of blood glucose level using wristband PPG signal and physiological parameters. Biomedical Signal Processing and Control, 78, 103876. https://doi.org/10.1016/j.bspc.2022.103876
Google Scholar
Sathish, D., Poojary, S. S., Shetty, S., Acharya, P. H., & Kabekody, S. (2024). Non-invasive diabetes detection system using photoplethysmogram signals. In S. Tiwari, M. C. Trivedi, M. L. Kolhe, & B. K. Singh (Eds.), Advances in Data and Information Sciences (Vol. 796, pp. 457–467). Springer Nature Singapore. https://doi.org/10.1007/978-981-99-6906-7_39
Google Scholar
Sen Gupta, S., Kwon, T.-H., Hossain, S., & Kim, K.-D. (2021). Towards non-invasive blood glucose measurement using machine learning: An all-purpose PPG system design. Biomedical Signal Processing and Control, 68, 102706. https://doi.org/10.1016/j.bspc.2021.102706
Google Scholar
Shaan, B., Prabha, A., & Yadav, J. (2023). Pulse decomposition analysis based non-invasive diabetes detection system. In S. M. Thampi, J. Mukhopadhyay, M. Paprzycki, & K.-C. Li (Eds.), International Symposium on Intelligent Informatics (Vol. 333, pp. 291-302). Springer Nature Singapore. https://doi.org/10.1007/978-981-19-8094-7_22
Google Scholar
Shaan, B., Yadav, J., & Prabha, A. (2022). ML based non-invasive diabetes detection system using pulse decomposition analysis of PPG signal. 2022 8th International Conference on Signal Processing and Communication (ICSC) (pp. 417-422). IEEE. https://doi.org/10.1109/ICSC56524.2022.10009195
Google Scholar
Shashikant, R., Chaskar, U., Phadke, L., & Patil, C. (2021). Gaussian process-based kernel as a diagnostic model for prediction of type 2 diabetes mellitus risk using non-linear heart rate variability features. Biomedical Engineering Letters, 11(3), 273-286. https://doi.org/10.1007/s13534-021-00196-7
Google Scholar
Singha, S. K., & Ahmad, M. (2021). Noninvasive heart rate and blood glucose level estimation using photoplethysmography. 2021 International Conference on Information and Communication Technology for Sustainable Development (ICICT4SD) (pp. 151-155). IEEE. https://doi.org/10.1109/ICICT4SD50815.2021.9396849
Google Scholar
Srinivasan, V. B., & Foroozan, F. (2021). Deep Learning based non-invasive diabetes predictor using Photoplethysmography signals. 2021 29th European Signal Processing Conference (EUSIPCO) (pp. 1256-1260). IEEE. https://doi.org/10.23919/EUSIPCO54536.2021.9616351
Google Scholar
Susana, E., Ramli, K., Murfi, H., & Apriantoro, N. H. (2022). Non-invasive classification of blood glucose level for early detection diabetes based on photoplethysmography signal. Information, 13(2), 59. https://doi.org/10.3390/info13020059
Google Scholar
Susana, E., Ramli, K., Purnamasari, P. D., & Apriantoro, N. H. (2023). Non-invasive classification of blood glucose level based on photoplethysmography using time-frequency analysis. Information, 14(3), 145. https://doi.org/10.3390/info14030145
Google Scholar
Zanelli, S., Yacoubi, M. A. E., Hallab, M., & Ammi, M. (2023). Type 2 diabetes detection with light CNN from single raw PPG wave. IEEE Access, 11, 57652-57665. https://doi.org/10.1109/ACCESS.2023.3274484
Google Scholar
Authors
Reehana SHAIKskreehana.15@gmail.com
VIT-AP UNIVERSITY India
https://orcid.org/0000-0002-2189-3616
Statistics
Abstract views: 65PDF downloads: 22
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles published in Applied Computer Science are open-access and distributed under the terms of the Creative Commons Attribution 4.0 International License.
Similar Articles
- Puppala Praneeth, Majety Sathvika, Vivek Kommareddy, Madala Sarath, Saran Mallela, Koneru Suvarna Vani, Prasun Chkrabarti, CLASSIFICATION OF PARKINSON'S DISEASE IN BRAIN MRI IMAGES USING DEEP RESIDUAL CONVOLUTIONAL NEURAL NETWORK , Applied Computer Science: Vol. 19 No. 2 (2023)
- Konrad BIERCEWICZ, Mariusz BORAWSKI, Anna BORAWSKA, Jarosław DUDA, DETERMINING THE DEGREE OF PLAYER ENGAGEMENT IN A COMPUTER GAME WITH ELEMENTS OF A SOCIAL CAMPAIGN USING COGNITIVE NEUROSCIENCE TECHNIQUES , Applied Computer Science: Vol. 18 No. 4 (2022)
- Grzegorz SUCHANEK, Roman FILIPEK, COMPUTATIONAL FLUID DYNAMICS (CFD) AIDED DESIGN OF A MULTI-ROTOR FLYING ROBOT FOR LOCATING SOURCES OF PARTICULATE MATTER POLLUTION , Applied Computer Science: Vol. 18 No. 3 (2022)
- Konrad PIETRYKOWSKI, Paweł KARPIŃSKI, SIMULATION STUDY OF HYDRODYNAMIC CAVITATION IN THE ORIFICE FLOW , Applied Computer Science: Vol. 18 No. 3 (2022)
- Manar AL-ABAJI, Zohair AL-AMEEN, SHARPNESS IMPROVEMENT OF MAGNETIC RESONANCE IMAGES USING A GUIDED-SUBSUMED UNSHARP MASK FILTER , Applied Computer Science: Vol. 20 No. 4 (2024)
- Mohanad ABDULHAMID, Njagi KINYUA, SOFTWARE FOR RECOGNITION OF CAR NUMBER PLATE , Applied Computer Science: Vol. 16 No. 1 (2020)
- Karolina FERYSIUK, Karolina M. WÓJCIAK, THE SPECTROPHOTOMETRIC ANALYSIS OF ANTIOXIDANT PROPERTIES OF SELECTED HERBS IN VISION-PRO™ UV-VIS , Applied Computer Science: Vol. 15 No. 4 (2019)
- Jarosław WIKAREK, Paweł SITEK, Mieczysław JAGODZIŃSKI, A DECLARATIVE APPROACH TO SHOP ORDERS OPTIMIZATION , Applied Computer Science: Vol. 15 No. 4 (2019)
- Marcin TOMCZYK, Anna PLICHTA, Mariusz MIKULSKI, APPLICATION OF IMAGE ANALYSIS TO THE IDENTIFICATION OF MASS INERTIA MOMENTUM IN ELECTROMECHANICAL SYSTEM WITH CHANGEABLE BACKLASH ZONE , Applied Computer Science: Vol. 15 No. 3 (2019)
- Quirino ESTRADA, Dariusz SZWEDOWICZ, Julio C. VERGARA, José SOLIS, Miguel A. PAREDES, Lara WIEBE, Jesús M. SILVA, NUMERICAL SIMULATIONS OF SANDWICH STRUCTURES UNDER LATERAL COMPRESSION , Applied Computer Science: Vol. 15 No. 2 (2019)
<< < 1 2 3 4 5 6 7 8 9 10 > >>
You may also start an advanced similarity search for this article.
