Research Area:  Machine Learning

Abstract:

Early detection of heart disease is an important challenge since 17.3 million people yearly lose their lives due to heart diseases. Besides, any error in diagnosis of cardiac disease can be dangerous and risks an individual life. Accurate diagnosis is therefore critical in cardiology. Data Mining (DM) classification techniques have been used to diagnosis heart diseases but still limited by some challenges of data quality such as inconsistencies, noise, missing data, outliers, high dimensionality and imbalanced data. Data preprocessing (DP) techniques were therefore used to prepare data with the goal of improving the performance of heart disease DM based prediction systems.

Keywords:  
Data mining
Data preprocessing
Cardiology
Cardiac datasets
Noise
Missing data
Outliers
High dimensionality
Imbalanced data

Author(s) Name:  H.Benhar, A.Idri, and J.L.Fernández-Alemán

Publisher name:  Elsevier B.V.

DOI:  https://doi.org/10.1016/j.cmpb.2020.105635

Volume Information:  Volume 195, 105635

Paper Link:   https://www.sciencedirect.com/science/article/abs/pii/S0169260720314681