Missing Value Imputation Using Stratified Supervised Learning for Cardiovascular Data

Davis Dn,Rahman Mm

doi:10.21767/2472-1956.100013

Abstract

Missing Value Imputation Using Stratified Supervised Learning for Cardiovascular Data

Highlights

Legacy medical datasets are rich source of information and knowledge, and there is a growing trend with research funders expecting the data resulting from clinical trials to be used beyond the originating study
Machine learning methods can be used for predicting missing values; for example by using rule induction algorithm in which rules are induced from the original complete data set, with missing attribute values ignored
The results are compared with some other non-stratified machine learning based missing value imputation methods using decision tree, SVM, K

Summary

Introduction

Legacy medical datasets are rich source of information and knowledge, and there is a growing trend with research funders expecting the data resulting from clinical trials to be used beyond the originating study. Machine learning methods can be used for predicting missing values; for example by using rule induction algorithm in which rules are induced from the original complete data set, with missing attribute values ignored. The results are compared with some other non-stratified machine learning based missing value imputation methods using decision tree, SVM, K-. Stratified machine learning based missing value imputation rules are later applied to the incomplete data for predicting the missing attribute values. The values of class attribute are generated according to the following heuristic model [30]: an instance (cardiovascular patient) is classified into “high” if the patient’s death or severe cardiovascular event (e.g. stroke, myocardial relapse or cardiovascular arrest) appears within 30 days after an operation. The k-NN is one of the simplest machine learning algorithms where an object is classified by a majority vote of its neighbours, where the object being allocated to the class most common amongst its “k” nearest neighbours (k is a positive integer, typically small)

Experiments

Findings

Conclusion