Dm-Health App:Diabetes Diagnosis Using Machine Learning with Smartphone  

作　　者：Elias Hossain Mohammed Alshehri Sultan Almakdi Hanan Halawani Md.Mizanur Rahman Wahidur Rahman Sabila Al Jannat Nadim Kaysar Shishir Mia 

机构地区：[1]Department of Software Engineering,Daffodil International University,Dhaka,1207,Bangladesh [2]Department of Computer Science,Najran University,Najran,55461,Saudi Arabia [3]Department of Computer Science and Engineering,Rajshahi University of Engineering and Technology,Rajshahi,6204,Bangladesh [4]Department of Computer Science and Engineering,Mawlana Bhashani Science and Technology University,Santosh,Tangail,1902,Bangladesh [5]Department of Computer Science and Engineering,BRAC University,Dhaka,1212,Bangladesh [6]Department of Computer Science and Engineering,World University Bangladesh,Dhaka,1230,Bangladesh

出　　处：《Computers, Materials & Continua》2022年第7期1713-1746,共34页计算机、材料和连续体（英文）

摘　　要：Diabetes Mellitus is one of the most severe diseases,and many studies have been conducted to anticipate diabetes.This research aimed to develop an intelligent mobile application based on machine learning to determine the diabetic,pre-diabetic,or non-diabetic without the assistance of any physician or medical tests.This study’s methodology was classified into two the Diabetes Prediction Approach and the Proposed System Architecture Design.The Diabetes Prediction Approach uses a novel approach,Light Gradient Boosting Machine(LightGBM),to ensure a faster diagnosis.The Proposed System ArchitectureDesign has been combined into sevenmodules;the Answering Question Module is a natural language processing Chabot that can answer all kinds of questions related to diabetes.The Doctor Consultation Module ensures free treatment related to diabetes.In this research,90%accuracy was obtained by performing K-fold cross-validation on top of the K nearest neighbor’s algorithm(KNN)&LightGBM.To evaluate the model’s performance,Receiver Operating Characteristics(ROC)Curve and Area under the ROC Curve(AUC)were applied with a value of 0.948 and 0.936,respectively.This manuscript presents some exploratory data analysis,including a correlation matrix and survey report.Moreover,the proposed solution can be adjustable in the daily activities of a diabetic patient.

关 键 词：Machine learning diabetes-prediction support vector machine(SVM) LightGBM EHEALTH ROC-AUC 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] R587.1[自动化与计算机技术—控制科学与工程]