An Efficient Prediction System for Diabetes Disease Based on Deep Neural Network

Tawfik Beghriche, Mohamed Djerioui, Youcef Brik, Bilal Attallah, Samir Brahim Belhaouari and Hocine Cherifi

Complexity, 2021, vol. 2021, 1-14

Abstract: One of the main reasons for disability and premature mortality in the world is diabetes disease, which can cause different sorts of damage to organs such as kidneys, eyes, and heart arteries. The deaths by diabetes are increasing each year, so the need to develop a system that can effectively diagnose diabetes patients becomes inevitable. In this work, an efficient medical decision system for diabetes prediction based on Deep Neural Network (DNN) is presented. Such algorithms are state-of-the-art in computer vision, language processing, and image analysis, and when applied in healthcare for prediction and diagnosis purposes, these algorithms can produce highly accurate results. Moreover, they can be combined with medical knowledge to improve decision-making effectiveness, adaptability, and transparency. A performance comparison between the DNN algorithm and some well-known machine learning techniques as well as the state-of-the-art methods is presented. The obtained results showed that our proposed method based on the DNN technique provides promising performances with an accuracy of 99.75% and an F1-score of 99.66%. This improvement can reduce time, efforts, and labor in healthcare services as well as increasing the final decision accuracy.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:hin:complx:6053824

DOI: 10.1155/2021/6053824

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