Accurate Diabetes Risk Stratification Using Machine Learning: Role of Missing Value and Outliers

Md Maniruzzaman,Md Menhazul Abedin,Md Al-Mehedihasan,Jasjit S Suri,Harman S Suri,Md Jahanur Rahman,Ayman El-Baz

doi:10.1007/s10916-018-0940-7

Abstract

Diabetes mellitus is a group of metabolic diseases in which blood sugar levels are too high. About 8.8% of the world was diabetic in 2017. It is projected that this will reach nearly 10% by 2045. The major challenge is that when machine learning-based classifiers are applied to such data sets for risk stratification, leads to lower performance. Thus, our objective is to develop an optimized and robust machine learning (ML) system under the assumption that missing values or outliers if replaced by a median configuration will yield higher risk stratification accuracy. This ML-based risk stratification is designed, optimized and evaluated, where: (i) the features are extracted and optimized from the six feature selection techniques (random forest, logistic regression, mutual information, principal component analysis, analysis of variance, and Fisher discriminant ratio) and combined with ten different types of classifiers (linear discriminant analysis, quadratic discriminant analysis, naïve Bayes, Gaussian process classification, support vector machine, artificial neural network, Adaboost, logistic regression, decision tree, and random forest) under the hypothesis that both missing values and outliers when replaced by computed medians will improve the risk stratification accuracy. Pima Indian diabetic dataset (768 patients: 268 diabetic and 500 controls) was used. Our results demonstrate that on replacing the missing values and outliers by group median and median values, respectively and further using the combination of random forest feature selection and random forest classification technique yields an accuracy, sensitivity, specificity, positive predictive value, negative predictive value and area under the curve as: 92.26%, 95.96%, 79.72%, 91.14%, 91.20%, and 0.93, respectively. This is an improvement of 10% over previously developed techniques published in literature. The system was validated for its stability and reliability. RF-based model showed the best performance when outliers are replaced by median values.

Highlights

Diabetes mellitus (DM) is known as diabetes in which blood glucose levels are too high [1]
Sixty systems have been designed by cross combination of ten classifiers (LDA, quadratic discriminant analysis (QDA), naïve Bayes (NB), Gaussian process classification (GPC), support vector machine (SVM), artificial neural network (ANN), Adaboost, Logistic regression (LR), decision tree (DT), and Random forest (RF)) and six feature selection techniques (RF, LR,Mutual information (MI), Principal component analysis (PCA), ANOVA, and Fisher discriminant ratio (FDR)) and their performances have been compared
The number of features has been selected with help of 0.90 cutoffs points for PCA while t-test has been adopted for LR, MI, FDR, respectively, and F-test for ANOVA

Summary

Introduction

Diabetes mellitus (DM) is known as diabetes in which blood glucose levels are too high [1]. There were about 1.5 million deaths directly due to diabetes and 2.2 million deaths due to cardiovascular diseases, chronic kidney disease, and tuberculosis in 2012 [3]. In type I diabetes, the body does not produce proper insulin. It is diagnosed in children and young adults [6]. Type II diabetes usually develops in adults over 45 years, and in young age children, adolescents and young adults. With type II diabetes, the pancreas does not produce enough insulin. The third type of diabetes is gestational diabetes. Pregnant women, who never had diabetes before, but have high blood glucose levels during pregnancy are diagnosed with gestational diabetes

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