Dual Multi Scale Attention Network Optimized With Archerfish Hunting Optimization Algorithm for Diabetics Prediction

Diabetes is a chronic disease that occurs when the body cannot regulate blood sugar levels. Nowadays, the screening tests for diabetes are developed using multivariate regression methods. An increasing amount of data is automatically collected to provide an opportunity for creating challenging and accurate prediction modes that are updated constantly with the help of machine learning techniques. In this manuscript, a Dual Multi Scale Attention Network optimized with Archerfish Hunting Optimization Algorithm is proposed for Diabetes Prediction (DMSAN-AHO-DP). Here, the data is gathered through PIMA Indian Diabetes Dataset (PIDD). The collected data is fed towards the preprocessing to remove the noise of input data and improves the data quality by using Contrast Limited Adaptive Histogram Equalization Filtering (CLAHEF) method. Then the preprocessed data are fed to Multi-Level Haar Wavelet Features Fusion Network (MHWFFN) based feature extraction. Then the extracted data is supplied to the Dual Multi Scale Attention Network (DMSAN) for diabetic or non-diabetic classification. The hyper parameter of Dual Multi Scale Attention Network is tuned with Archerfish Hunting Optimization (AHO) algorithm, which classifies diabetic or non-diabetic accurately. The proposed DMSAN-AHO-DP technique is implemented in Python. The efficacy of the DMSAN-AHO-DP approach is examined with some metrics, like Accuracy, F-scores, Sensitivity, Specificity, Precision, Recall, Computational time. The DMSAN-AHO-DP technique achieves 23.52%, 36.12%, 31.12% higher accuracy and 16.05%, 21.14%, 31.02% lesser error rate compared with existing models: Enhanced Deep Neural Network based Model for Diabetes Prediction (EDNN-DP), Indian PIMA Dataset using Deep Learning for Diabetes Prediction (ANN-DP), and Enhanced Support Vector Machine with Deep Neural Network Learning strategies for Diabetes Prediction (SVM-DNN-DP).