Accurate Diabetes Risk Stratification Using Machine Learning: Role of Missing Value and Outliers.
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 ri...
| Publicado en: | Journal of Medical Systems Vol. 42; no. 5; pp. 1 - 2 |
|---|---|
| Autores principales: | , , , , , , |
| Formato: | algorithm equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
May2018
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=129370618&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 129370618 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: May2018 vid: 42 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 129370618 129370618 129370618 10.1007/s10916-018-0940-7 129370618 ppf: 1 ppct: 1 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Accurate Diabetes Risk Stratification Using Machine Learning: Role of Missing Value and Outliers. aug: au: Maniruzzaman, Md. Rahman, Md. Jahanur Al-MehediHasan, Md. Suri, Harman S. Abedin, Md. Menhazul El-Baz, Ayman Suri, Jasjit S. affil: Department of Statistics, University of Rajshahi, Rajshahi, Bangladesh sug: subj: Risk Assessment Diabetes Mellitus Classification Machine Learning Human Female Adult Native Americans Logistic Regression Neural Networks (Computer) Decision Trees Experimental Studies Factor Analysis Adult: 19-44 years Female ab: 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. pubtype: Academic Journal doctype: algorithm equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|