Current Techniques for Diabetes Prediction: Review and Case Study

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.

Comparative performance analysis of quantum machine learning with deep learning for diabetes prediction

“…Being motivated by this, in recent years, a number of ML and DL based frameworks have been proposed for the prediction of diabetes such as logistic regression, artificial neural network (ANN), linear discriminant analysis (LDA), naive Bayes (NB), support vector machine (SVM), decision tree (DT), AdaBoost (AB), J48, k-nearest neighbors (k-NN), quadratic discriminant analysis (QDA), random forest (RF), multilayer perceptron (MLP), general regression neural network, and radial basis function (RBF) [11]. These techniques have been employed with various dimensionality reduction (such as principal component analysis) and crossvalidation (for example k-fold cross-validation) techniques along with the mechanism for filling missing values and rejecting outliers to uplift the performance of ML and DL models.…”

Combinatorial K-Means Clustering as a Machine Learning Tool Applied to Diabetes Mellitus Type 2

“…However, the application of ML algorithms was applied in solving and mitigating the effect on the society of this problem. The article by Marie-Sainte [ 1 ] graphed all the ML and DL techniques based on efforts for diabetes predictions published in the last years. The endorsement is to use these proper classification and prediction models in diabetes disease and improve the robustness of them by developing more complex applicable models.…”

Machine learning algorithms for Diabetes prediction and neural network method for blood glucose measurement

“…(3) Since glucose is an optically active substance, by the non-invasive technique, the physical properties such as optical, acoustic and electrical properties of the fluid or underlying tissues can be measured. (4) There are many non-invasive techniques include mid-infrared (MIR) (5) , near-infrared (NIR) spectroscopy (6) , Raman spectroscopy (7) , Impedance spectroscopy (8) ,Polarimetry (9) . In recent years NIRS (Near Infrared Spectroscopy) has come up as a potential technique for non-invasive glucose monitoring due to low absorption and more penetration of Near Infrared light into the skin.NIR spectroscopy uses the light in the 750-2500 nm region, which interrogates the tissue with low energy radiation.…”