NOVEL MULTI-MODAL OBSTRUCTION MODULE FOR DIABETES MELLITUS CLASSIFICATION USING EXPLAINABLE MACHINE LEARNING
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 differ...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Polish Association for Knowledge Promotion
2024-12-01
|
| Series: | Applied Computer Science |
| Subjects: | |
| Online Access: | https://ph.pollub.pl/index.php/acs/article/view/6527 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841552801212137472 |
|---|---|
| author | Reehana SHAIK Ibrahim SIDDIQUE |
| author_facet | Reehana SHAIK Ibrahim SIDDIQUE |
| author_sort | Reehana SHAIK |
| collection | DOAJ |
| description |
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.
|
| format | Article |
| id | doaj-art-cf02f044bddd4f5ebd0d64caad9827cf |
| institution | Kabale University |
| issn | 2353-6977 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Polish Association for Knowledge Promotion |
| record_format | Article |
| series | Applied Computer Science |
| spelling | doaj-art-cf02f044bddd4f5ebd0d64caad9827cf2025-01-09T12:44:46ZengPolish Association for Knowledge PromotionApplied Computer Science2353-69772024-12-0120410.35784/acs-2024-39NOVEL MULTI-MODAL OBSTRUCTION MODULE FOR DIABETES MELLITUS CLASSIFICATION USING EXPLAINABLE MACHINE LEARNINGReehana SHAIK0https://orcid.org/0000-0002-2189-3616Ibrahim SIDDIQUE 1https://orcid.org/0000-0003-3310-6090VIT-AP UNIVERSITYVIT AP UNIVERSITY 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. https://ph.pollub.pl/index.php/acs/article/view/6527Diabetes MellitusElectrocardiogram (ECG)Non-Invasive methodPhotoplethysmographyFeature ExtractionExplainable ML |
| spellingShingle | Reehana SHAIK Ibrahim SIDDIQUE NOVEL MULTI-MODAL OBSTRUCTION MODULE FOR DIABETES MELLITUS CLASSIFICATION USING EXPLAINABLE MACHINE LEARNING Applied Computer Science Diabetes Mellitus Electrocardiogram (ECG) Non-Invasive method Photoplethysmography Feature Extraction Explainable ML |
| title | NOVEL MULTI-MODAL OBSTRUCTION MODULE FOR DIABETES MELLITUS CLASSIFICATION USING EXPLAINABLE MACHINE LEARNING |
| title_full | NOVEL MULTI-MODAL OBSTRUCTION MODULE FOR DIABETES MELLITUS CLASSIFICATION USING EXPLAINABLE MACHINE LEARNING |
| title_fullStr | NOVEL MULTI-MODAL OBSTRUCTION MODULE FOR DIABETES MELLITUS CLASSIFICATION USING EXPLAINABLE MACHINE LEARNING |
| title_full_unstemmed | NOVEL MULTI-MODAL OBSTRUCTION MODULE FOR DIABETES MELLITUS CLASSIFICATION USING EXPLAINABLE MACHINE LEARNING |
| title_short | NOVEL MULTI-MODAL OBSTRUCTION MODULE FOR DIABETES MELLITUS CLASSIFICATION USING EXPLAINABLE MACHINE LEARNING |
| title_sort | novel multi modal obstruction module for diabetes mellitus classification using explainable machine learning |
| topic | Diabetes Mellitus Electrocardiogram (ECG) Non-Invasive method Photoplethysmography Feature Extraction Explainable ML |
| url | https://ph.pollub.pl/index.php/acs/article/view/6527 |
| work_keys_str_mv | AT reehanashaik novelmultimodalobstructionmodulefordiabetesmellitusclassificationusingexplainablemachinelearning AT ibrahimsiddique novelmultimodalobstructionmodulefordiabetesmellitusclassificationusingexplainablemachinelearning |