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All Journal Indian Journal of Forensic Medicine & Toxicology
Aliakbar Tajari Siahmarzkooh
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Public Health

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ACO-based Type 2 Diabetes Detection using Artificial Neural Networks Aliakbar Tajari Siahmarzkooh
Indian Journal of Forensic Medicine & Toxicology Vol. 15 No. 1 (2021): Indian Journal of Forensic Medicine & Toxicology
Publisher : Institute of Medico-legal Publications Pvt Ltd

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37506/ijfmt.v15i1.13666

Abstract

Background: Type 2 diabetes is one of the most common diseases among people. Early diagnosis andtreatment can reduce mortality and morbidity. So far, various solutions have been proposed to predict thistype of disease.Materials and Method: In this paper, a method for diagnosing diabetes was proposed using the AntColony Optimization (ACO) algorithm. To this end, data set properties are first reduced using artificialneural network features and then prepared for classification purpose. Finally, some components of accuracyassessment on the proposed system were calculated.Results: The simulation results show that by adjusting the parameters of ANN and ACO, about 3.2% betterprediction accuracy is obtained than other researches.Conclusion: The results of experiments represent that the proposed method is proper for health managementin diabetes.
Type 2 Diabetes Prediction using Gray Wolf Optimization Algorithm Aliakbar Tajari Siahmarzkooh
Indian Journal of Forensic Medicine & Toxicology Vol. 15 No. 3 (2021): Indian Journal of Forensic Medicine & Toxicology
Publisher : Institute of Medico-legal Publications Pvt Ltd

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37506/ijfmt.v15i3.15980

Abstract

Background: Increasing the number of diabetic patients and the ignorance of most of these patients aboutthe dangers arising from it is a challenge that threatens human lives.Materials and Method: In this paper, a new solution based on the Gray Wolf Optimization (GWO) algorithmfor predicting type 2 diabetes is presented. The main purpose of the proposed method is to increase theaccuracy of prediction and also to reduce the probability of getting stuck in local optimal points. In moredetail, the proposed method consists of two parts: 1- data preprocessing including data preparation andnoise cancellation and 2- data classification using gray wolf algorithm. The Pima Indians Diabetes dataset inMATLAB simulation environment was used to analyze the data and compare the research results.Results: The simulation results show that by adjusting the parameters of the gray wolf algorithm, about 6%better prediction accuracy is obtained than other researches.Conclusion: Also, for a more accurate evaluation of the proposed method, two other datasets have beenused for testing. The results of experiments show that the proposed model for health management in diabetesis effective.
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