Design, development, and usability evaluation of a smartphone-based application for nutrition management in patients with type II diabetes

A stacked ensemble machine learning approach for the prediction of diabetes

Objectives

Diabetes has become a leading cause of mortality in both developed and developing countries, impacting a growing number of individuals worldwide. As the prevalence of the disease continues to rise, researchers have diligently worked towards developing accurate diabetes prediction models. The primary aim of this study is to utilize a diverse set of machine learning algorithms to detect the presence of diabetes, particularly in females, at an early stage. By leveraging these methods, this research seeks to provide physicians with valuable tools to identify the disease early, enabling timely interventions and improving patient outcomes.

Methods

In this study, some state-of-the-art machine learning techniques, such as random forest classifiers with gridsearchCV, XGBoost, NGBoost, Bagging, LightGBM, and AdaBoost classifiers, were employed. These models were chosen as the base layer of our proposed stacked ensemble model because of their high accuracy. Before feeding the data into the models, the dataset was preprocessed to ensure optimal performance and obtain improved results.

Results

The accuracy achieved in this study was 92.91%, which demonstrates its competitiveness with the existing approaches. Moreover, the utilization of the Shapley additive explanation (SHAP) facilitated the interpretation of machine learning models.

Conclusion

We anticipate that these findings will be beneficial to healthcare providers, stakeholders, students, and researchers involved in diabetes prediction research and development.

Smartphone applications for nutrition Support: A systematic review of the target outcomes and main functionalities

INTRODUCTION

A proper nutrition is essential for human life. Recently, special attention on this topic has been given in relation to three health statuses: obesity, malnutrition and specific diseases that can be related to food or treated with specific diets. Mobile technology is often used to assist users that wish to regulate their eating habits, and identifying which fields of application have been explored the most by the app developers and which main functionalities have been adopted can be useful in view of future app developments.

METHODS

We selected 322 articles mentioning nutrition support apps through a literature database search, all of which have undergone an initial screening. After the exclusion of papers that were already reviews, not presenting apps or not focused on nutrition, not relevant or not developed for human subjects, 100 papers were selected for subsequent analyses that aimed at identifying the main treated conditions, outcome measures and functionalities implemented in the Apps.

RESULTS

Of the selected studies, 33 focus on specific diseases, 24 on obesity, 2 on malnutrition and 41 on other targets (e.g., weight/diet control). Type 2 diabetes is the most targeted disease, followed by gestational diabetes, hypertension, colorectal cancer and CVDs which all were targeted by more than one app. Most Apps include self-monitoring and coaching functionalities, educational content and artificial intelligence (AI) tools are slightly less common, whereas counseling, gamification and questionnaires are the least implemented. Body weight and calories/nutrients were the most common general outcome measures, while glycated hemoglobin (HbA1c) was the most common clinical outcome. No statistically significant differences in the effectiveness of the different functionalities were found.

CONCLUSION

The use of mobile technology to improve nutrition has been widely explored in the last years, especially for weight control and specific diseases like diabetes; however, other food-related conditions such as Irritable Bowel Diseases appear to be less targeted by newly developed smartphone apps and their related studies. All different kinds of functionalities appear to be equally effective, but further specific studies are needed to confirm the results.