An Approach to Find Missing Values in Medical Datasets

TL;DR

This paper presents a new imputation measure for effectively handling missing categorical data in medical datasets, improving the preprocessing phase of data mining.

Contribution

It introduces a novel imputation method specifically designed for missing categorical values in medical datasets, demonstrated through a case study.

Findings

The proposed measure accurately estimates missing values.

Improved data quality enhances subsequent data mining tasks.

Case study validates the effectiveness of the approach.

Abstract

Mining medical datasets is a challenging problem before data mining researchers as these datasets have several hidden challenges compared to conventional datasets.Starting from the collection of samples through field experiments and clinical trials to performing classification,there are numerous challenges at every stage in the mining process. The preprocessing phase in the mining process itself is a challenging issue when, we work on medical datasets. One of the prime challenges in mining medical datasets is handling missing values which is part of preprocessing phase. In this paper, we address the issue of handling missing values in medical dataset consisting of categorical attribute values. The main contribution of this research is to use the proposed imputation measure to estimate and fix the missing values. We discuss a case study to demonstrate the working of proposed measure.