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 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.