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Diabetic Retinopathy01:27

Diabetic Retinopathy

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DefinitionDiabetic retinopathy is a microvascular complication of diabetes affecting the retinal blood vessels.Risk FactorsDiabetic retinopathy is present in almost all individuals with type 1 diabetes and more than 60% of those with type 2 diabetes after two decades of disease.The risk increases with poor glycemic control, hypertension, dyslipidemia, smoking, pregnancy, and puberty.Although cataracts and glaucoma are also more frequent in people with diabetes, retinopathy remains the leading...
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Deep Neural Networks for Image-Based Dietary Assessment
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Dual Multi Scale Attention Network Optimized With Archerfish Hunting Optimization Algorithm for Diabetics Prediction.

Helina Rajini Suresh1, K Anita Davamani2,3, Hemalatha Chandrasekaran3

  • 1Department of Electronics and Communication Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, Tamil Nadu, India.

Microscopy Research and Technique
|December 2, 2024
PubMed
Summary

A new machine learning model, DMSAN-AHO-DP, improves diabetes prediction accuracy using the PIMA Indian Diabetes Dataset. This advanced technique offers higher accuracy and lower error rates compared to existing methods for detecting diabetes.

Keywords:
archerfish hunting optimizationdiabetics predictiondual multi scale attention networkmulti‐level Haar wavelet features fusion network

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Area of Science:

  • Medical Informatics
  • Machine Learning
  • Data Science

Background:

  • Diabetes mellitus is a chronic condition affecting blood sugar regulation.
  • Current diabetes screening relies on multivariate regression methods.
  • Advancements in data collection and machine learning offer opportunities for improved prediction models.

Purpose of the Study:

  • To propose a novel Dual Multi Scale Attention Network optimized with Archerfish Hunting Optimization Algorithm (DMSAN-AHO-DP) for accurate diabetes prediction.
  • To enhance data quality and feature extraction for improved classification performance.
  • To evaluate the efficacy of the proposed model against existing diabetes prediction techniques.

Main Methods:

  • Utilized the PIMA Indian Diabetes Dataset (PIDD) for model training and evaluation.
  • Applied Contrast Limited Adaptive Histogram Equalization Filtering (CLAHEF) for data preprocessing and noise reduction.
  • Employed a Multi-Level Haar Wavelet Features Fusion Network (MHWFFN) for feature extraction.
  • Implemented a Dual Multi Scale Attention Network (DMSAN) for binary classification (diabetic/non-diabetic).
  • Optimized DMSAN hyperparameters using the Archerfish Hunting Optimization (AHO) algorithm.
  • Developed the DMSAN-AHO-DP model in Python.

Main Results:

  • The DMSAN-AHO-DP model demonstrated superior performance in diabetes prediction.
  • Achieved significant improvements in accuracy (23.52%, 36.12%, 31.12% higher) compared to EDNN-DP, ANN-DP, and SVM-DNN-DP models.
  • Showcased reduced error rates (16.05%, 21.14%, 31.02% lower) compared to the benchmark models.
  • Evaluated performance using metrics including Accuracy, F-scores, Sensitivity, Specificity, Precision, Recall, and Computational time.

Conclusions:

  • The proposed DMSAN-AHO-DP model offers a highly accurate and efficient approach for diabetes prediction.
  • The integration of multi-scale attention mechanisms and heuristic optimization significantly enhances predictive capabilities.
  • This method provides a promising advancement in leveraging machine learning for early diabetes detection and management.