An Analytical Predictive Models and Secure Web-Based Personalized Diabetes Monitoring System

Abstract

Diabetes, in all of its types, costs countries of all income levels unacceptably enormous personal, societal, and economic expenses. To predict type-2 diabetes, this work aimed to develop an analytical predictive model based on machine learning techniques and a web-based personalized diabetes monitoring system. The history of a patient will be collected and ready for analysis purposes based on machine learning techniques by continuously monitoring the patient's vital data. A diabetes monitoring system is proposed by utilizing the patient's QR card that allows the patients and doctors to be connected to the Internet of Things. So, they can deliver real-time information (such as insulin records) about their health status and can visit different healthcare institutions. The proposed system can help doctors to make data-driven decisions and enhance patients' treatment. Several machine learning algorithms that are Decision Tree, Support Vector Classifier, Random Forest, Gradient Boosting, Multi-layer Perceptron's, Artificial Neural Network, k-Nearest Neighbors, Logistic Regression, and Naive Bayes are used. The proposed analytical model is evaluated based on two different datasets a synthetic dataset and PIMA Diabetes Dataset. The performance of the classification models was analyzed in terms of accuracy, recall, and precision based on the cross-validation strategy. The findings are found that the ANN model has better prediction accuracy than other models. The evaluation findings are analyzed and compared with other existing models. The system has dashboard graphs displaying the number of patients in Saudi Arabia cities. It also contains visualized graphs that include more detailed classifications for patients' states (Normal, Pre-Diabetes, and Diabetes).

Evaluating the performance of an AI-powered VBAC prediction system within a decision-aid birth choice platform for shared decision-making

“…The interactive web service (IWS) was tailored for AI-powered predictions, utilizing medical record data and a machine learning model, 14 notably CatBoost, with finely tuned hyperparameters. Its primary function is to anticipate the probability of a successful VBAC and identify potential birth-related risks for pregnant women.…”

An intelligent diabetes classification and perception framework based on ensemble and deep learning method

“…Using the patient’s QR card, researchers in the study ( Marzouk, Alluhaidan & El Rahman, 2022 ) developed a system for monitoring diabetes and communicating treatment updates to medical professionals. The author performed an experiment on PIMA and diabetes synthetic data sets.…”

“… Benbelkacem & Atmani (2019) developed SVM, naïve Bayes (NB), and RF models for diabetes detection. Marzouk, Alluhaidan & El Rahman (2022) selected optimal features in PIMA using Autoencoder and applied deep neural network (DNN). The PIMA dataset is frequently used in machine learning and predictive analytics to develop and assess diabetes diagnosis systems.…”