Machine Learning Models in Diagnosis and Treatment of Diabetes

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

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