Abstract
Diabetes is one of the most common diseases worldwide. Many Machine Learning (ML) techniques have been utilized in predicting diabetes in the last couple of years. The increasing complexity of this problem has inspired researchers to explore the robust set of Deep Learning (DL) algorithms. The highest accuracy achieved so far was 95.1% by a combined model CNN-LSTM. Even though numerous ML algorithms were used in solving this problem, there are a set of classifiers that are rarely used or even not used at all in this problem, so it is of interest to determine the performance of these classifiers in predicting diabetes. Moreover, there is no recent survey that has reviewed and compared the performance of all the proposed ML and DL techniques in addition to combined models. This article surveyed all the ML and DL techniques-based diabetes predictions published in the last six years. In addition, one study was developed that aimed to implement those rarely and not used ML classifiers on the Pima Indian Dataset to analyze their performance. The classifiers obtained an accuracy of 68%–74%. The recommendation is to use these classifiers in diabetes prediction and enhance them by developing combined models.
Highlights
Diabetes is one of the frequent diseases that targets the elderly population worldwide
The main datasets used in the related works are Electrocardiograms (ECG) [8], a private dataset collected from three different locations in Kosovo [18], Breath Dataset [46], a dataset from UCI [4], and the Pima dataset which used by more than 10 studies
Researchers are passionate to try different types of classifiers and build new models with an effort to enhance the accuracy of diabetes prediction
Summary
Diabetes is one of the frequent diseases that targets the elderly population worldwide. According to the International Diabetes Federation, 451 million people across the world were diabetic in 2017. The expectations are that this number will increase to affect 693 million people in the coming 26 years [1]. Early detection and treatment of diabetes can prevent complications and assist in reducing the risk of severe health problems. Many researchers in the bioinformatics field have attempted to address this disease and tried to create systems and tools that will help in diabetes prediction. They either built prediction models using different types of machine learning algorithms such as classification or association algorithms.
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