A comprehensive review of machine learning techniques on diabetes detection

Abstract

Diabetes mellitus has been an increasing concern owing to its high morbidity, and the average age of individual affected by of individual affected by this disease has now decreased to mid-twenties. Given the high prevalence, it is necessary to address with this problem effectively. Many researchers and doctors have now developed detection techniques based on artificial intelligence to better approach problems that are missed due to human errors. Data mining techniques with algorithms such as - density-based spatial clustering of applications with noise and ordering points to identify the cluster structure, the use of machine vision systems to learn data on facial images, gain better features for model training, and diagnosis via presentation of iridocyclitis for detection of the disease through iris patterns have been deployed by various practitioners. Machine learning classifiers such as support vector machines, logistic regression, and decision trees, have been comparative discussed various authors. Deep learning models such as artificial neural networks and recurrent neural networks have been considered, with primary focus on long short-term memory and convolutional neural network architectures in comparison with other machine learning models. Various parameters such as the root-mean-square error, mean absolute errors, area under curves, and graphs with varying criteria are commonly used. In this study, challenges pertaining to data inadequacy and model deployment are discussed. The future scope of such methods has also been discussed, and new methods are expected to enhance the performance of existing models, allowing them to attain greater insight into the conditions on which the prevalence of the disease depends.

Development and validation of a machine learning-augmented algorithm for diabetes screening in community and primary care settings: A population-based study

“…Remarkably, there is a rich library of available machine learning methods used for developing models to deal with a specific problem, and it is crucial to select an appropriate ML method to improve the performance of the models ( 4 ). Recent studies indicate that diabetes prediction models developed based on images, electronic health records, or structured data obtained from their societies, using machine learning algorithms such as Decision Tree, Naive Bayes, SVM, ANN, etc., achieve superior performance and demonstrate their potential to be helpful for diabetes screening ( 38 , 39 ). We employed seven ML algorithms for diabetes screening using data from our population-based study in China, including LGBM, ANN, SVM, RF, KNN, CDKNN and LR, which are reported to have good performances in developing predictive models with high accuracy in recent studies ( 40 – 44 ).…”

Development and Evaluation of a Predictive Diagnostic System for Dengue Fever using Machine Learning Techniques

“…While [7], [11], studied the utilization of soft computing techniques in diagnosing tropical febrile diseases, and found that ensemble techniques were used more frequently than single machine learning techniques, with Dengue fever being the most studied disease. The other works [7], [10]- [13] study aimed to address the diabetes problem effectively by utilizing arti cial intelligence-based detection techniques, such as density-based spatial clustering [15] of applications with noise and ordering points to identify the cluster structure, machine vision systems, and iridocyclitis for detection of the disease through iris patterns.…”

A Federated Learning-Inspired Evolutionary Algorithm: Application to Glucose Prediction

“…Both the above issues assume noticeable importance in the medical domain, e.g., diabetes management. Several techniques have been investigated to discover data-driven glucose forecasting models, ranging from approaches based on regression [ 35 , 36 , 37 , 38 , 39 ] to those that handle the prediction as a classification problem [ 29 , 40 , 41 , 42 ]. These techniques can be classified as explainable or interpretable based on the techniques employed for discovering the learning model.…”