Improving the precision of heart diseases detection has been investigated by many
researchers in the literature. Such improvement induced by the overwhelming health care …

Accepted: 15/Apr/2019, Published: 30/Apr/2019 Abstract-The problems of missing values in
the field of data mining have become emerging areas of research in recent years. It has …

Missing data imputation is a critical part of data cleaning tasks and vital for learning from
incomplete data. This paper proposes a novel cooperative clustering imputation (CCI) …

Missing values are quite common in medical records. Fixing missing values is a challenging
task to data mining analysts as any error in imputation process leads to potential hazards. In …

Aim: This proposed work is used to develop an improved and robust machine learning
model for predicting Myocardial Infarction (MI) could have substantial clinical impact …

Analysis of a medical dataset having missing values and then filling the missing values
through different approaches exists in the literature. However, the classification accuracies …

Classification is a major task in machine learning and data mining. Many real-world datasets
suffer from the unavoidable issue of missing values. Classification with incomplete data has …

Heart disease diagnosis is based on the patient's signs and symptoms and is affected by
several factors such as cholesterol level, blood pressure, obesity, smoking habit, and other …

Imputation of medical records is a prime challenge when we deal with medical records. The
imputed values affect classification of new medical records. This is because of this reason …

Missing data problem has been recognized as a major issue, since it affects the validity of
datasets and surveys. Since every collection of data aims to collect information, a dataset …