Identification of People with Diabetes Treatment through Lipids Profile Using Machine Learning Algorithms

Abstract

Diabetes incidence has been a problem, because according with the World Health Organization and the International Diabetes Federation, the number of people with this disease is increasing very fast all over the world. Diabetic treatment is important to prevent the development of several complications, also lipid profile monitoring is important. For that reason the aim of this work is the implementation of machine learning algorithms that are able to classify cases, that corresponds to patients diagnosed with diabetes that have diabetes treatment, and controls that refers to subjects who do not have diabetes treatment but some of them have diabetes, bases on lipids profile levels. Logistic regression, K-nearest neighbor, decision trees and random forest were implemented, all of them were evaluated with accuracy, sensitivity, specificity and AUC-ROC curve metrics. Artificial neural network obtain an acurracy of 0.685 and an AUC value of 0.750, logistic regression achieve an accuracy of 0.729 and an AUC value of 0.795, K-nearest neighbor gets an accuracy of 0.669 and an AUC value of 0.709, on the other hand, decision tree reached an accuracy pg 0.691 and a AUC value of 0.683, finally random forest achieve an accuracy of 0.704 and an AUC curve of 0.776. The performance of all models was statistically significant, but the best performance model for this problem corresponds to logistic regression.

Machine Learning for Screening Microvascular Complications in Type 2 Diabetic Patients Using Demographic, Clinical, and Laboratory Profiles

“…Most recently, machine learning has emerged in many biomedical applications as a promising tool to aid in decision-making regarding many diseases, including diabetes. In [29], the authors managed to implement a machine learning approach based on decision trees to identify the diabetic patients with or without treatment procedures from their lipid profiles. In addition, Koren et al [30] developed a trained model capable of diagnosing diabetic patients with drugs that lower blood glucose levels.…”

Spatiotemporal Analysis and Risk Assessment Model Research of Diabetes among People over 45 Years Old in China

“…Recent studies in the health field have adopted machine learning and deep learning algorithms. Since machine-learning approaches perform well in predicting diabetes, they are gaining traction in the health profession [ 22 , 23 ]. This research hoped to analyze the regional differences of diabetes among people over 45 years old in China, and to assess diabetes risk [ 24 ], thereby aiming to provide reference for the formulation of diabetes prevention and treatment programs.…”

Comparativa de modelos multivariados basados en aprendizaje automático para la predicción del riesgo de diabetes en etapas tempranas

“…La especificidad corresponde a la proporción de verdaderos negativos, es decir, los sujetos con una condición negativa que fueron correctamente clasificados y se calcula con la Ecuación (2), donde VN son los verdaderos negativos y FP son los falsos positivos [17].…”

“…Ésta indica la confiabilidad del modelo al clasificar los datos dentro de una clase midiendo el número de términos correctamente reconocidos respecto al total de términos predichos, sean verdaderos o falsos [18]. Por otro lado, la exactitud calcula el rendimiento promedio de los algoritmos, como se muestra en la ecuación (4), el propósito de esta métrica es calcular el porcentaje de muestras que son clasificadas correctamente [17].…”