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Data preprocessing for heart disease classification: A systematic literature review - 2020

Data Preprocessing For Heart Disease Classification: A Systematic Literature Review

Data preprocessing for heart disease classification | S - Logix

Research Area:  Machine Learning


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.

Data mining
Data preprocessing
Cardiac datasets
Missing data
High dimensionality
Imbalanced data

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

Journal name:  Computer Methods and Programs in Biomedicine

Conferrence name:  

Publisher name:  Elsevier B.V.


Volume Information:  Volume 195, 105635