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Diagnostics
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Machine Learning Models in Diagnosis and Treatment of Diabetes
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Machine Learning Models in Diagnosis and Treatment of Diabetes

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 6072

Special Issue Editor

Prof. Dr. Mohiuddin Ahmad

E-Mail Website
Guest Editor
Department of Electrical and Electronic Engineering, Khulna University of Engineering & Technology (KUET), Khulna 9203, Bangladesh
Interests: signal and image processing for disease analysis; computer vision and pattern recognition; ICT and AI in eHealth; human motion analysis; energy conversion

Special Issue Information

Dear Colleagues,

Diabetes is a chronic disease that affects millions of people worldwide, and its prevalence is increasing at an alarming rate. The disease is characterized by high blood sugar levels, which can lead to a range of complications, such as heart disease, kidney failure, blindness, nerve damage, etc.

In recent years, there has been a growing interest in the use of machine learning models for the diagnosis and treatment of diabetes. Machine learning is a type of artificial intelligence that allows computers to learn and improve from experience without being explicitly programmed. This makes it particularly useful for analyzing large numbers of data, such as medical records and health sensor data, to identify patterns and make predictions.

Machine learning models can be used to develop predictive tools for diabetes diagnosis, risk stratification, and personalized treatment. These models can analyze patient data, such as medical history, blood glucose levels, and genetic information, to identify risk factors and predict the likelihood of an individual developing diabetes. They can also be used to develop personalized treatment plans based on individual patient characteristics, such as age, weight, and other health conditions.

Overall, the use of machine learning models in the diagnosis and treatment of diabetes has the potential to improve patient outcomes and reduce healthcare costs by enabling more accurate and personalized care.

Scopes / List of topics:

This Special Issue will explore, but is not restricted to, the following topics:

(i). Diabetics data collection;

(ii). Data preprocessing techniques for machine learning models (MLMs) in diabetes diagnosis and treatment;

(iii). MLMs’ application in predicting an individual’s risk of developing diabetes;

(iv). Detection and diagnosis of diabetes by using MLMs;

(v). Early diagnosis of diabetics;

(vi). Personalized diabetes management using MLMs;

(vii). Predictive models for the onset and progression of diabetes;

(viii). Insulin dose prediction and glucose control using ML models;

(ix). Performance and accuracy evaluation of MLMs in diabetes diagnosis and treatment;

(x). Interpreting machine learning models in diabetes diagnosis and treatment;

(xi). DLMs in diabetes diagnosis and treatment;

(xii). Monitoring of diabetes.

Prof. Dr. Mohiuddin Ahmad
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diagnostics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • diabetics
  • machine learning models (MLMs)
  • deep learning models
  • risk assessment
  • detection
  • diagnosis
  • insulin dose control
  • glucose control
  • diabetics treatment
  • monitoring of diabetics

Published Papers (4 papers)

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Research

12 pages, 759 KiB  
Article
Machine Learning and Deep Learning Models for Nocturnal High- and Low-Glucose Prediction in Adults with Type 1 Diabetes
by Roman M. Kozinetz, Vladimir B. Berikov, Julia F. Semenova and Vadim V. Klimontov
Diagnostics 2024, 14(7), 740; https://doi.org/10.3390/diagnostics14070740 - 30 Mar 2024
Viewed by 486
Abstract
Glucose management at night is a major challenge for people with type 1 diabetes (T1D), especially for those managed with multiple daily injections (MDIs). In this study, we developed machine learning (ML) and deep learning (DL) models to predict nocturnal glucose within the [...] Read more.
Glucose management at night is a major challenge for people with type 1 diabetes (T1D), especially for those managed with multiple daily injections (MDIs). In this study, we developed machine learning (ML) and deep learning (DL) models to predict nocturnal glucose within the target range (3.9–10 mmol/L), above the target range, and below the target range in subjects with T1D managed with MDIs. The models were trained and tested on continuous glucose monitoring data obtained from 380 subjects with T1D. Two DL algorithms—multi-layer perceptron (MLP) and a convolutional neural network (CNN)—as well as two classic ML algorithms, random forest (RF) and gradient boosting trees (GBTs), were applied. The resulting models based on the DL and ML algorithms demonstrated high and similar accuracy in predicting target glucose (F1 metric: 96–98%) and above-target glucose (F1: 93–97%) within a 30 min prediction horizon. Model performance was poorer when predicting low glucose (F1: 80–86%). MLP provided the highest accuracy in low-glucose prediction. The results indicate that both DL (MLP, CNN) and ML (RF, GBTs) algorithms operating CGM data can be used for the simultaneous prediction of nocturnal glucose values within the target, above-target, and below-target ranges in people with T1D managed with MDIs. Full article
(This article belongs to the Special Issue Machine Learning Models in Diagnosis and Treatment of Diabetes)
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15 pages, 518 KiB  
Article
Evaluating Deep Q-Learning Algorithms for Controlling Blood Glucose in In Silico Type 1 Diabetes
by Miguel Tejedor, Sigurd Nordtveit Hjerde, Jonas Nordhaug Myhre and Fred Godtliebsen
Diagnostics 2023, 13(19), 3150; https://doi.org/10.3390/diagnostics13193150 - 07 Oct 2023
Viewed by 973
Abstract
Patients with type 1 diabetes must continually decide how much insulin to inject before each meal to maintain blood glucose levels within a healthy range. Recent research has worked on a solution for this burden, showing the potential of reinforcement learning as an [...] Read more.
Patients with type 1 diabetes must continually decide how much insulin to inject before each meal to maintain blood glucose levels within a healthy range. Recent research has worked on a solution for this burden, showing the potential of reinforcement learning as an emerging approach for the task of controlling blood glucose levels. In this paper, we test and evaluate several deep Q-learning algorithms for automated and personalized blood glucose regulation in an in silico type 1 diabetes patient with the goal of estimating and delivering proper insulin doses. The proposed algorithms are model-free approaches with no prior information about the patient. We used the Hovorka model with meal variation and carbohydrate counting errors to simulate the patient included in this work. Our experiments compare different deep Q-learning extensions showing promising results controlling blood glucose levels, with some of the proposed algorithms outperforming standard baseline treatment. Full article
(This article belongs to the Special Issue Machine Learning Models in Diagnosis and Treatment of Diabetes)
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18 pages, 3051 KiB  
Article
Improving the Accuracy of Continuous Blood Glucose Measurement Using Personalized Calibration and Machine Learning
by Ranjita Kumari, Pradeep Kumar Anand and Jitae Shin
Diagnostics 2023, 13(15), 2514; https://doi.org/10.3390/diagnostics13152514 - 27 Jul 2023
Cited by 1 | Viewed by 1595
Abstract
Despite tremendous developments in continuous blood glucose measurement (CBGM) sensors, they are still not accurate for all patients with diabetes. As glucose concentration in the blood is <1% of the total blood volume, it is challenging to accurately measure glucose levels in the [...] Read more.
Despite tremendous developments in continuous blood glucose measurement (CBGM) sensors, they are still not accurate for all patients with diabetes. As glucose concentration in the blood is <1% of the total blood volume, it is challenging to accurately measure glucose levels in the interstitial fluid using CBGM sensors due to within-patient and between-patient variations. To address this issue, we developed a novel data-driven approach to accurately predict CBGM values using personalized calibration and machine learning. First, we scientifically divided measured blood glucose into smaller groups, namely, hypoglycemia (<80 mg/dL), nondiabetic (81–115 mg/dL), prediabetes (116–150 mg/dL), diabetes (151–181 mg/dL), severe diabetes (181–250 mg/dL), and critical diabetes (>250 mg/dL). Second, we separately trained each group using different machine learning models based on patients’ personalized parameters, such as physical activity, posture, heart rate, breath rate, skin temperature, and food intake. Lastly, we used multilayer perceptron (MLP) for the D1NAMO dataset (training to test ratio: 70:30) and grid search for hyperparameter optimization to predict accurate blood glucose concentrations. We successfully applied our proposed approach in nine patients with type 1 diabetes and observed that the mean absolute relative difference (MARD) decreased from 17.8% to 8.3%. Full article
(This article belongs to the Special Issue Machine Learning Models in Diagnosis and Treatment of Diabetes)
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40 pages, 2241 KiB  
Article
Classification of Diabetes Using Feature Selection and Hybrid Al-Biruni Earth Radius and Dipper Throated Optimization
by Amel Ali Alhussan, Abdelaziz A. Abdelhamid, S. K. Towfek, Abdelhameed Ibrahim, Marwa M. Eid, Doaa Sami Khafaga and Mohamed S. Saraya
Diagnostics 2023, 13(12), 2038; https://doi.org/10.3390/diagnostics13122038 - 12 Jun 2023
Cited by 6 | Viewed by 1990
Abstract
Introduction: In public health, machine learning algorithms have been used to predict or diagnose chronic epidemiological disorders such as diabetes mellitus, which has reached epidemic proportions due to its widespread occurrence around the world. Diabetes is just one of several diseases for which [...] Read more.
Introduction: In public health, machine learning algorithms have been used to predict or diagnose chronic epidemiological disorders such as diabetes mellitus, which has reached epidemic proportions due to its widespread occurrence around the world. Diabetes is just one of several diseases for which machine learning techniques can be used in the diagnosis, prognosis, and assessment procedures. Methodology: In this paper, we propose a new approach for boosting the classification of diabetes based on a new metaheuristic optimization algorithm. The proposed approach proposes a new feature selection algorithm based on a dynamic Al-Biruni earth radius and dipper-throated optimization algorithm (DBERDTO). The selected features are then classified using a random forest classifier with its parameters optimized using the proposed DBERDTO. Results: The proposed methodology is evaluated and compared with recent optimization methods and machine learning models to prove its efficiency and superiority. The overall accuracy of diabetes classification achieved by the proposed approach is 98.6%. On the other hand, statistical tests have been conducted to assess the significance and the statistical difference of the proposed approach based on the analysis of variance (ANOVA) and Wilcoxon signed-rank tests. Conclusions: The results of these tests confirmed the superiority of the proposed approach compared to the other classification and optimization methods. Full article
(This article belongs to the Special Issue Machine Learning Models in Diagnosis and Treatment of Diabetes)
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