[Retracted] Machine Learning Implementation of a Diabetic Patient Monitoring System Using Interactive E‐App

Abstract

Lifestyle influences morbidity and mortality rates in the world. Physical activity, a healthy weight, and a healthy diet are key preventative health behaviours that help reduce the risk of developing type 2 diabetes and its complications, such as cardiovascular disease. A healthy lifestyle has been shown to prevent or delay chronic diseases and their complications, but few people follow all recommended self-management behaviours. This work seeks to improve knowledge of factors affecting type 2 diabetes self-management and prevention through lifestyle changes. This paper describes the design, development, and testing of a diabetes self-management mobile app. The app tracked dietary consumption and health data. Bluetooth movement data from a pair of wearable insole devices are used to track carbohydrate intake, blood glucose, medication adherence, and physical activity. Two machine learning models were constructed to recognise sitting and standing. The SVM and decision tree models were 86% accurate for these tasks. The decision tree model is used in a real-time activity classification app. It is exciting to see more and more mobile health self-management apps being used to treat chronic diseases.

Prevalence and Correlation of Metabolic Syndrome in Patients with Bipolar Disorder in NGHA, Riyadh

“…In Saudi Arabia, this is the first study to assess metabolic syndrome in association with patients suffering from bipolar disorder. Only one research from Saudi Arabia assesses the prevalence of metabolic syndrome in patients with psychiatric disorders not bipolar disorder, however [ 12 , 13 ]. Throughout the time of economic prosperity, Saudi Arabia's population's dietary patterns went through several alterations.…”

Diagnosing Cancer Using IOT and Machine Learning Methods

“…This method employs many trees instead of a decision tree. A random vector determines the value of each tree in the forest [ 12 , 13 ]. The number of trees can be planned.…”

Automation of Cephalometrics Using Machine Learning Methods

“…For each point, a box with the average distances to Pogonio and Silla points is set up, and the size of the box is Using the same idea to avoid false detections for the Pogonio and Silla points was proposed. To do so, the average X and Y coordinates of their locations were calculated using the training data, and the same procedure was used for the rest of the cephalometric points [15]. Figure 10 shows a test image with two high probability zones for the Gonion point, one correct and one false.…”