Decision Support System for Diabetes Mellitus through Machine Learning Techniques

نویسنده

  • Tarik A. Rashid
چکیده

recently, the diseases of diabetes mellitus have grown into extremely feared problems that can have damaging effects on the health condition of their sufferers globally. In this regard, several machine learning models have been used to predict and classify diabetes types. Nevertheless, most of these models attempted to solve two problems; categorizing patients in terms of diabetic types and forecasting blood surge rate of patients. This paper presents an automatic decision support system for diabetes mellitus through machine learning techniques by taking into account the above problems, plus, reflecting the skills of medical specialists who believe that there is a great relationship between patient’s symptoms with some chronic diseases and the blood sugar rate. Data sets are collected from Layla Qasim Clinical Center in Kurdistan Region, then, the data is cleaned and proposed using feature selection techniques such as Sequential Forward Selection and the Correlation Coefficient, finally, the refined data is fed into machine learning models for prediction, classification, and description purposes. This system enables physicians and doctors to provide diabetes mellitus (DM) patients good health treatments and

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Machine learning algorithms in air quality modeling

Modern studies in the field of environment science and engineering show that deterministic models struggle to capture the relationship between the concentration of atmospheric pollutants and their emission sources. The recent advances in statistical modeling based on machine learning approaches have emerged as solution to tackle these issues. It is a fact that, input variable type largely affec...

متن کامل

Smart Diabetic Screening and Managing Software, A Novel Decision Support System

Background: Diabetes is a serious chronic disease, and its increasing prevalence is a global concern. If diabetes mellitus is left untreated, poor control of blood glucose may cause long-term complications. A big challenge encountered by clinicians is the clinical management of diabetes. Many IT-based interventions such ad CDSS have been made to improve the adherence to the standard care for ch...

متن کامل

Application of ensemble learning techniques to model the atmospheric concentration of SO2

In view of pollution prediction modeling, the study adopts homogenous (random forest, bagging, and additive regression) and heterogeneous (voting) ensemble classifiers to predict the atmospheric concentration of Sulphur dioxide. For model validation, results were compared against widely known single base classifiers such as support vector machine, multilayer perceptron, linear regression and re...

متن کامل

An Early Detection Method of Type-2 Diabetes Mellitus in Public Hospital

Diabetes is a chronic disease and major problem of morbidity and mortality in developing countries. The International Diabetes Federation estimates that 285 million people around the world have diabetes. This total is expected to rise to 438 million within 20 years. Type-2 diabetes mellitus (T2DM) is the most common type of diabetes and accounts for 90-95% of all diabetes. Detection of T2DM fro...

متن کامل

Designing an intelligent system for diagnosing type 2 diabetes using the data mining approach: brief report

Background: Diabetes mellitus has several complications. The Late diagnosis of diabetes in people leads to the spread of complications. Therefore, this study has been done to determine the possibility of predicting diabetes type 2 by using data mining techniques. Methods: This is a descriptive-analytic study that was conducted as a cross-sectional study. The study population included people re...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2016