Supervised Learning Models for Early Detection of Albuminuria Risk in Type-2 Diabetes Mellitus Patients

TL;DR

This study develops and compares supervised learning models to predict albuminuria risk in T2DM patients, with the MLP model showing the best performance for early screening.

Contribution

It introduces a supervised learning approach using multiple algorithms on a private dataset to predict albuminuria risk in diabetic patients, highlighting the effectiveness of MLP.

Findings

MLP achieved accuracy of 0.74 and f1-score of 0.75.

Support Vector Machine and other models performed less effectively.

The models can assist in early screening for diabetic nephropathy.

Abstract

Diabetes, especially T2DM, continues to be a significant health problem. One of the major concerns associated with diabetes is the development of its complications. Diabetic nephropathy, one of the chronic complication of diabetes, adversely affects the kidneys, leading to kidney damage. Diagnosing diabetic nephropathy involves considering various criteria, one of which is the presence of a pathologically significant quantity of albumin in urine, known as albuminuria. Thus, early prediction of albuminuria in diabetic patients holds the potential for timely preventive measures. This study aimed to develop a supervised learning model to predict the risk of developing albuminuria in T2DM patients. The selected supervised learning algorithms included Na\"ive Bayes, Support Vector Machine (SVM), decision tree, random forest, AdaBoost, XGBoost, and Multi-Layer Perceptron (MLP). Our private dataset, comprising 184 entries of diabetes complications risk factors, was used to train the algorithms. It consisted of 10 attributes as features and 1 attribute as the target (albuminuria). Upon conducting the experiments, the MLP demonstrated superior performance compared to the other algorithms. It achieved accuracy and f1-score values as high as 0.74 and 0.75, respectively, making it suitable for screening purposes in predicting albuminuria in T2DM. Nonetheless, further studies are warranted to enhance the model's performance.