Learning Difference Equations with Structured Grammatical Evolution for Postprandial Glycaemia Prediction

People with diabetes must carefully monitor their blood glucose levels, especially after eating. Blood glucose regulation requires a proper combination of food intake and insulin boluses. Glucose prediction is vital to avoid dangerous post-meal complications in treating individuals with diabetes. Although traditional methods, such as artificial neural networks, have shown high accuracy rates, sometimes they are not suitable for developing personalised treatments by physicians due to their lack of interpretability. In this study, we propose a novel glucose prediction method emphasising interpretability: Interpretable Sparse Identification by Grammatical Evolution. Combined with a previous clustering stage, our approach provides finite difference equations to predict postprandial glucose levels up to two hours after meals. We divide the dataset into four-hour segments and perform clustering based on blood glucose values for the twohour window before the meal. Prediction models are trained for each cluster for the two-hour windows after meals, allowing predictions in 15-minute steps, yielding up to eight predictions at different time horizons. Prediction safety was evaluated based on Parkes Error Grid regions. Our technique produces safe predictions through explainable expressions, avoiding zones D (0.2% average) and E (0%) and reducing predictions on zone C (6.2%). In addition, our proposal has slightly better accuracy than other techniques, including sparse identification of non-linear dynamics and artificial neural networks. The results demonstrate that our proposal provides interpretable solutions without sacrificing prediction accuracy, offering a promising approach to glucose prediction in diabetes management that balances accuracy, interpretability, and computational efficiency

Gramáticas evolutivas para la predicción de hipoglucemias en diabetes

Trabajo de Fin de Grado en Ingeniería Informática, Facultad de Informática UCM, Departamento Ingeniería del Software e Inteligencia Artificial, Curso 2021/2022. https://github.com/ABSysGroup/jeco/tree/TFG_GE4HYPODiabetic patients have to manage their blood sugar correctly to prevent complications. One such complication is hypoglycemia or low blood sugar, which occurs when the blood glucose concentration goes below a certain threshold. A hypoglycemic episode needs to be rectified before it becomes harmful and can be a very distressing situation for the patient. The main goal of this study is to program Structured Grammatical Evolution and Dynamic Structured Grammatical Evolution algorithms and use them to generate models for the prediction of hypoglycemic episodes in patients with diabetes. The algorithms will be used to obtain a white-box model made up of if-then-else statements that given some input data, comprised of the blood glucose and exercise readings of the patients from the previous 2 hours, optimizes a logical relation between these variables. The resulting formula will be able to determine if the patient is going to have a hypoglycemic episode in a 30, 60, 90 and 120 minutes prediction horizon.Los pacientes con diabetes deben controlar correctamente su nivel de azúcar en sangre para evitar complicaciones. Una de estas complicaciones es la hipoglucemia o nivel bajo de azúcar en sangre, que ocurre cuando la concentración de glucosa en la sangre cae por debajo de cierto umbral. Un episodio de hipoglucemia debe corregirse antes de que se vuelva dañino y puede ser una situación muy angustiosa para el paciente. El objetivo principal de este estudio es programar los algoritmos de Gramáticas Evolutivas Estructuradas y Gramáticas Evolutivas Estructuradas Dinámicas, y utilizarlos para generar modelos de predicción de episodios hipoglucémicos en pacientes con diabetes. Los algoritmos se utilizarán para obtener un modelo de caja blanca formado por sentencias if-then-else que, dados unos datos de entrada, compuestos por el nivel de la glucosa en sangre y las lecturas de datos de ejercicio de los pacientes de las 2 horas anteriores, optimiza una relación lógica entre estas variables. La fórmula resultante se usa para determinar si el paciente va a tener un episodio de hipoglucemia en un plazo de 30, 60, 90 y 120 minutos.Depto. de Ingeniería de Software e Inteligencia Artificial (ISIA)Fac. de InformáticaTRUEunpu

A Proposed Method to Identify the Occurrence of Diabetes in Human Body using Machine Learning Technique

Advanced machine-learning techniques are often used for reasoning-based diagnosis and advanced prediction system within the healthcare industry. The methods and algorithms are based on the historical clinical data and factbased Medicare evaluation. Diabetes is a global problem. Each year people are developing diabetes and due to diabetes, a lot of people are going for organ amputation. According to the World Health Organization (WHO), there is a sharp rise in number of people developing diabetes. In 1980, it was estimated that 180 million people with diabetes worldwide. This number has risen from 108 million to 422 million in 2014. WHO also reported that 1.6 million deaths in 2016 due to diabetes. Diabetes occurs due to insufficient production of insulin from pancreas. Several research show that unhealthy diet, smoking, less exercise, Body Mass Index (BMI) are the primary cause of diabetes. This paper shows the use of machine learning that can identify a patient of being diabetic or non-diabetic based on previous clinical data. In this article, a method is shown to analyze and compare the relationship between different clinical parameters such as age, BMI, Diet-chart, systolic Blood Pressure etc. After evaluating all the factors this research work successfully combined all the related factors in a single mathematical equation which is very effective to analyze the risk percentage and risk evaluation based on given input parameters by the participants or users

Pathway-Based Multi-Omics Data Integration for Breast Cancer Diagnosis and Prognosis.

Ph.D. Thesis. University of Hawaiʻi at Mānoa 2017

Machine learning for data integration in human gut microbiome

Recent studies have demonstrated that gut microbiota plays critical roles in various human diseases. High-throughput technology has been widely applied to characterize the microbial ecosystems, which led to an explosion of different types of molecular profiling data, such as metagenomics, metatranscriptomics and metabolomics. For analysis of such data, machine learning algorithms have shown to be useful for identifying key molecular signatures, discovering potential patient stratifications, and particularly for generating models that can accurately predict phenotypes. In this review, we first discuss how dysbiosis of the intestinal microbiota is linked to human disease development and how potential modulation strategies of the gut microbial ecosystem can be used for disease treatment. In addition, we introduce categories and workflows of different machine learning approaches, and how they can be used to perform integrative analysis of multi-omics data. Finally, we review advances of machine learning in gut microbiome applications and discuss related challenges. Based on this we conclude that machine learning is very well suited for analysis of gut microbiome and that these approaches can be useful for development of gut microbe-targeted therapies, which ultimately can help in achieving personalized and precision medicine