Diabetes Diagnosis Using Machine Learning

Abstract

Introduction: Diabetes is a disease associated with high levels of glucose in the blood. Diabetes make many kinds of complications, which also leads to a high rate of repeated admission of patients with diabetes. The aim of this study is to diagnose Diabetes with machine learning techniques.Material and Methods: The datasets of the article contain several medical predictor variables and one target variable, Outcome. Predictor variables includes the number of pregnancies the patient has had, their BMI, insulin level, age. The main objective of the machine learning models is to classify of the diabetes disease.Results: six classifiers have been also adapted and compared their performance based on accuracy, F1-score, recall, precision and AUC. And Finally, Adaboost has the most accuracy 83%.Conclusion: In this paper a performance comparison of different classifier models for classifying diagnosis is done. The models considered for comparison are logistic regression, Decision Tree, support vector machine (SVM), xgboost, Random forest and ada boost. Finally, in the comparison flow, Adaboost, Logistic Regression, SVM and Random Forest, usually has had a high amount; and their amounts has little differences normally.

IoT-Based Hybrid Ensemble Machine Learning Model for Efficient Diabetes Mellitus Prediction

“…Diabetes, often known to be diabetes mellitus (DM), is a group of metabolic illnesses characterized by persistently elevated blood sugar levels. Excessive urination, continuous thirst, and an increase in hunger are all symptoms of high blood sugar [ 1 ]. Diabetes, if not treated promptly, can lead to significant health problems in a person, such as hyperglycaemic, hyperosmolar condition, diabetic ketoacidosis, or even one of the results for death.…”

Applications of Machine Learning for Diabetes Prediction

“…Ensemble methods combine multiple weak classifiers to create a strong predictive model. AdaBoost [15] and Gradient Boosting [16] are popular ensemble techniques used in diabetes prediction. These methods iteratively train weak classifiers and assign higher weights to misclassified instances, focusing on the difficult samples.…”

An efficient stacking based NSGA-II approach for predicting type 2 diabetes

“…Diabetes has become a common disease leading to growing interest of researchers in optimization of predictive model for early detection. Several machine learning approaches and data mining techniques like artificial neural network (ANN), decision tree, KNN, SVM, extreme learning machine have become apparent and are being applied in aiding the prediction of T2DMection of diabetes [6]- [8]. Aseri et al [9] have used metaheuristic algorithms for classifying the covid-19 dataset available on Kaggle.…”