Posted: October 27th, 2023

Detection and Classification of Diabetes Type based on Machine Learning Techniques

Detection and Classification of Diabetes Type based on Machine Learning Techniques

Diabetes is a chronic metabolic disorder that affects the body’s ability to process blood glucose, also known as blood sugar. High blood glucose levels can lead to serious complications such as cardiovascular diseases, kidney failure, nerve damage, and vision loss. According to the World Health Organization, diabetes was the seventh leading cause of death in 2016: 2024 – Do my homework – Help write my assignment online, and it is estimated that 422 million people worldwide have diabetes [1].

There are two main types of diabetes: type 1 and type 2. Type 1 diabetes is an autoimmune disease that occurs when the pancreas produces little or no insulin, a hormone that regulates blood glucose levels. Type 1 diabetes usually develops in childhood or adolescence and requires lifelong insulin therapy. Type 2 diabetes is more common and occurs when the body becomes resistant to the effects of insulin or does not produce enough insulin. Type 2 diabetes usually develops in adulthood and is associated with obesity, physical inactivity, and genetic factors. Type 2 diabetes can be managed with lifestyle changes, oral medications, and sometimes insulin injections.

Early detection and classification of diabetes type are essential for preventing or delaying the onset of complications and improving the quality of life of patients. However, traditional methods for diagnosing diabetes, such as oral glucose tolerance test (OGTT), fasting plasma glucose (FPG), or glycated hemoglobin (HbA1c), are invasive, time-consuming, and costly. Moreover, these methods do not provide information about the type of diabetes, which requires further tests such as autoantibody tests or C-peptide tests.

Machine learning is a branch of artificial intelligence that enables computers to learn from data and make predictions or decisions without explicit programming. Machine learning techniques have been widely applied to various domains of healthcare, such as diagnosis, prognosis, treatment recommendation, and disease management [2]. Machine learning can also be used for detection and classification of diabetes type based on non-invasive or minimally invasive data, such as anthropometric measurements (e.g., height, weight, body mass index), demographic information (e.g., age, gender, ethnicity), lifestyle factors (e.g., smoking, alcohol consumption, physical activity), and biochemical indicators (e.g., blood pressure, cholesterol, triglycerides).

In this blog post, we will review some of the recent studies that have used machine learning techniques for detection and classification of diabetes type based on different types of data. We will also discuss the advantages and limitations of these techniques and provide some suggestions for future research directions.

Machine Learning Techniques for Detection and Classification of Diabetes Type

Machine learning techniques can be broadly classified into two categories: supervised learning and unsupervised learning. Supervised learning is a type of machine learning where the data is labeled with the desired output or class. The goal of supervised learning is to learn a function that maps the input data to the output label. Supervised learning techniques can be further divided into two subcategories: classification and regression. Classification is a type of supervised learning where the output label is discrete or categorical, such as yes/no, positive/negative, or type 1/type 2. Regression is a type of supervised learning where the output label is continuous or numerical, such as blood glucose level or HbA1c value.

Unsupervised learning is a type of machine learning where the data is unlabeled or does not have a predefined output. The goal of unsupervised learning is to discover hidden patterns or structures in the data without any prior knowledge or guidance. Unsupervised learning techniques can be further divided into two subcategories: clustering and dimensionality reduction. Clustering is a type of unsupervised learning where the data is grouped into clusters based on some similarity or distance measure. Clustering can be used to identify subgroups or segments of data that share common characteristics or behaviors. Dimensionality reduction is a type of unsupervised learning where the data is transformed into a lower-dimensional space while preserving the most relevant information or features. Dimensionality reduction can be used to reduce noise or redundancy in the data or to visualize high-dimensional data in a more comprehensible way.

The choice of machine learning technique depends on the type and availability of data, the research question or objective, and the evaluation criteria or metrics. In general, supervised learning techniques are more suitable for detection and classification tasks where the output label is known or predefined. Unsupervised learning techniques are more suitable for exploratory tasks where the output label is unknown or undefined.

In this section, we will review some examples of machine learning techniques that have been used for detection and classification of diabetes type based on different types of data.

Detection and Classification Based on Anthropometric Measurements

Anthropometric measurements are physical measurements of the human body that reflect its size, shape, and composition. Anthropometric measurements are easy to obtain, non-invasive,
and inexpensive. Some common anthropometric measurements include height, weight, body mass index (BMI), waist circumference, hip circumference, waist-to-hip ratio (WHR), and body fat percentage.

Several studies have used anthropometric measurements as input data for machine learning techniques to detect or classify diabetes type. For example, Wee et al. [3] used a deep neural network (DNN) to classify diabetes type based on height, weight, BMI, and WHR. They used the Pima Indian Diabetes Dataset (PIDD), which contains 768 records of female patients of Pima Indian heritage, with 268 diagnosed with diabetes and 500 without diabetes. The dataset also contains information about the number of pregnancies, plasma glucose concentration, diastolic blood pressure, triceps skinfold thickness, serum insulin level, diabetes pedigree function, and age. The authors used a 10-fold cross-validation method to evaluate the performance of the DNN and compared it with other machine learning techniques, such as support vector machine (SVM), k-nearest neighbor (KNN), decision tree (DT), and random forest (RF). They found that the DNN achieved the highest accuracy of 82.29%, followed by SVM (80.99%), RF (79.43%), KNN (77.47%), and DT (75.52%). They also found that the DNN was able to distinguish between type 1 and type 2 diabetes with an accuracy of 77.78%, while the other techniques failed to do so.

Another example is the study by Butt et al. [4], who used three different classifiers, namely RF, multilayer perceptron (MLP), and logistic regression (LR), to classify diabetes based on height, weight, BMI, WHR, and body fat percentage. They also used the PIDD as the input data and performed a 10-fold cross-validation method to evaluate the performance of the classifiers. They found that MLP outperformed the other classifiers with an accuracy of 86.08%, followed by RF (84.64%) and LR (83.85%). They also performed a predictive analysis using long short-term memory (LSTM), moving averages (MA), and LR to predict the blood glucose level of diabetic patients based on their anthropometric measurements. They found that LSTM achieved the highest accuracy of 87.26%, followed by MA (85.34%) and LR (83.56%).

These studies demonstrate that anthropometric measurements can be used as input data for machine learning techniques to detect or classify diabetes type with high accuracy. However, these studies also have some limitations that need to be addressed in future research. First, the PIDD is a relatively small and imbalanced dataset that may not be representative of the general population or other ethnic groups. Second, the PIDD does not provide information about the type of diabetes for each patient, which limits the ability to validate the classification results. Third, anthropometric measurements may not capture all the relevant factors or features that influence diabetes development or progression, such as genetic factors, environmental factors, or biochemical indicators.

Detection and Classification Based on Biochemical Indicators

Biochemical indicators are substances or molecules that can be measured in biological fluids or tissues, such as blood, urine, saliva, or hair. Biochemical indicators can reflect the physiological or metabolic status of an individual or a specific organ or system. Some common biochemical indicators include blood glucose level, HbA1c level, blood pressure, cholesterol level,
triglyceride level, creatinine level, urea level, and C-reactive protein level.

Several studies have used biochemical indicators as input data for machine learning techniques to detect or classify diabetes type. For example, Kumar et al. [5] used a hybrid model that combines K-means clustering and SVM to classify diabetes type based on FPG level, HbA1c level,
blood pressure level, cholesterol level, triglyceride level, and C-reactive protein level. They used a dataset that contains 1000 records of patients with different types of diabetes: type 1 (250 records), type 2 (250 records), gestational diabetes (250 records), and prediabetes (250 records). The dataset also contains information about age and gender for each patient. The authors used a stratified random sampling method to split the dataset into training set (70%) and testing set (30%). They applied K-means clustering to group the training data into four clusters based on their similarity or distance measure. Then they applied SVM to each cluster to train a classifier that can distinguish between different types of diabetes. They evaluated the performance of the hybrid model using accuracy, precision,
recall, F1-score, and receiver operating characteristic (ROC) curve metrics. They found that the hybrid model achieved an accuracy of 96%, a precision of 96%, a recall of 96%, an F1-score of 96%, and an area under the ROC curve (AUC) of 0.98.

Another example is the study by Singh et al. [6], who used an artificial neural network (ANN) to

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