Diabetes Prediction Using Deep Learning: A Comprehensive Approach Utilizing Feature Selection and Deep Neural Networks

Authors

  • Adnan Shafqat Department of Computer Science , Superior University, Lahore , Pakistan
  • Saira Afzal Department of Computers Science , The Sahara College , Narowal, Pakistan
  • Muhammad Haseeb Zia Department of Criminology and Forensics Sciences, LahoreGarrison University, Lahore, Pakistan.
  • Saima Zaib School of Mechanical and Manufacturing Engineering, National University of Science and Technology, Islamabad, Pakistan.
  • Muhammad Tahir Graduate School of Science and Engineering (Electronics), Karachi Institute of Economics and Technology (KIET), Karachi, Pakistan.
  • Aqsa Zehra Graduate School of Science and Engineering (Electronics), Karachi Institute of Economics and Technology (KIET), Karachi, Pakistan.

Keywords:

Diabetes Forecast, Feature Selection, Ant Lion Optimization (ALO), Metaheuristic Algorithms, Deep Neural Network (DNN)

Abstract

Diabetes is a disorder that has a significant impact on world health. In order to properly treat the illness and avoid complications, early identification is crucial. This paper presents a novel scheme for diabetes prediction based on Ant Lion Optimization(ALO)-enhanced deep learning feature selection. We conducted thorough data processing, able to handle missing values, specifying outliers, and validating the Pima Indian's diabetes-relevant data. The selection of pertinent features was optimizedthat use ALO, and the resulting deep neural network (DNN) was then providedwith classification training. The suggested model outperforms typical machine learning (ML) approaches, with an astonishing 96.50% accuracy. This prediction precision demonstrates the aim to expand predictive accuracy by integrating metaheuristic systems with DNNs. According to our findings, this technique is ideal for dramatically enhancing early diabetes diagnosis and delivering valuable knowledge for medical decisions

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Published

2024-09-29

How to Cite

Adnan Shafqat, Saira Afzal, Muhammad Haseeb Zia, Saima Zaib, Muhammad Tahir, & Aqsa Zehra. (2024). Diabetes Prediction Using Deep Learning: A Comprehensive Approach Utilizing Feature Selection and Deep Neural Networks. Journal of Computing & Biomedical Informatics. Retrieved from https://jcbi.org/index.php/Main/article/view/623

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Articles