A Novel Effective Ensemble Model for Early Detection of Coronary Artery Disease

One of the major types of cardiovascular diseases is Coronary Artery Disease (CAD). This study tackles the problem of CAD detection using a new accurate hybrid machine learning model. The proposed ensemble model combines several classical machine learning techniques. Our base algorithm is used with four different kernel functions (linear, polynomial, radial basis and sigmoid). The new model was applied to analyze the well-known Cleveland CAD dataset from the UCI repository. To improve the performance of the model, we first selected the most important features of this dataset using a genetic search algorithm. Second, we applied a multi-level filtering technique to balance the data using the ClassBalancer and Resample methods. Our model provided the average CAD prediction accuracy of 98.34% for the Cleveland data (the average was taken over the four kernel functions).

[1]  Yang Fu,et al.  Short-term wind power forecasts by a synthetical similar time series data mining method , 2018 .

[2]  Maciej Kusy,et al.  Probabilistic neural network training procedure based on Q(0)-learning algorithm in medical data classification , 2014, Applied Intelligence.

[3]  Jafar Habibi,et al.  Coronary artery disease detection using computational intelligence methods , 2016, Knowl. Based Syst..

[4]  U. Rajendra Acharya,et al.  IAPSO-AIRS: A novel improved machine learning-based system for wart disease treatment , 2019, Journal of Medical Systems.

[5]  Oumaima Terrada,et al.  Classification and Prediction of atherosclerosis diseases using machine learning algorithms , 2019, 2019 5th International Conference on Optimization and Applications (ICOA).

[6]  Mattias Ohlsson,et al.  Improving prediction of heart transplantation outcome using deep learning techniques , 2018, Scientific Reports.

[7]  Lin Lu,et al.  Machine Learning and Network Methods for Biology and Medicine , 2015, Comput. Math. Methods Medicine.

[8]  Kemal Polat,et al.  Automatic detection of heart disease using an artificial immune recognition system (AIRS) with fuzzy resource allocation mechanism and k , 2007, Expert Syst. Appl..

[9]  Moloud Abdar,et al.  CWV-BANN-SVM ensemble learning classifier for an accurate diagnosis of breast cancer , 2019, Measurement.

[10]  Novruz Allahverdi,et al.  Design of a hybrid system for the diabetes and heart diseases , 2008, Expert Syst. Appl..

[11]  U. Rajendra Acharya,et al.  Automated diagnosis of coronary artery disease using tunable-Q wavelet transform applied on heart rate signals , 2015, Knowl. Based Syst..

[12]  Jun Deng,et al.  Predicting non-melanoma skin cancer via a multi-parameterized artificial neural network , 2018, Scientific Reports.

[13]  Simon Fong,et al.  Improving the classification performance of biological imbalanced datasets by swarm optimization algorithms , 2016, The Journal of Supercomputing.

[14]  Kasturi Dewi Varathan,et al.  Identification of significant features and data mining techniques in predicting heart disease , 2019, Telematics Informatics.

[15]  Pierre Baldi,et al.  SSpro/ACCpro 5: almost perfect prediction of protein secondary structure and relative solvent accessibility using profiles, machine learning and structural similarity , 2014, Bioinform..

[16]  Mei-Ling Huang,et al.  An approach combining data mining and control charts-based model for fault detection in wind turbines , 2018 .

[17]  Abdulkadir Sengür,et al.  Effective diagnosis of heart disease through neural networks ensembles , 2009, Expert Syst. Appl..

[18]  Moloud Abdar,et al.  Using PSO Algorithm for Producing Best Rules in Diagnosis of Heart Disease , 2017, 2017 International Conference on Computer and Applications (ICCA).

[19]  Moloud Abdar,et al.  Performance analysis of classification algorithms on early detection of liver disease , 2017, Expert Syst. Appl..

[20]  Adeeb Noor,et al.  An Optimized Stacked Support Vector Machines Based Expert System for the Effective Prediction of Heart Failure , 2019, IEEE Access.

[21]  Pawel Plawiak,et al.  Novel genetic ensembles of classifiers applied to myocardium dysfunction recognition based on ECG signals , 2017, Swarm Evol. Comput..

[22]  Yi-Ping Phoebe Chen,et al.  Association rule mining to detect factors which contribute to heart disease in males and females , 2013, Expert Syst. Appl..

[23]  Kemal Polat,et al.  Diagnosis of heart disease using artificial immune recognition system and fuzzy weighted pre-processing , 2006, Pattern Recognit..

[24]  Kwong-Sak Leung,et al.  The Impact of Protein Structure and Sequence Similarity on the Accuracy of Machine-Learning Scoring Functions for Binding Affinity Prediction , 2018, Biomolecules.

[25]  Kavita Burse,et al.  Various Preprocessing Methods for Neural Network Based Heart Disease Prediction , 2019 .

[26]  Frantisek Babic,et al.  Predictive and descriptive analysis for heart disease diagnosis , 2017, 2017 Federated Conference on Computer Science and Information Systems (FedCSIS).

[27]  Yichuan Wang,et al.  An integrated big data analytics-enabled transformation model: Application to health care , 2018, Inf. Manag..

[28]  Moloud Abdar,et al.  Design of A Universal User Model for Dynamic Crowd Preference Sensing and Decision-Making Behavior Analysis , 2017, IEEE Access.

[29]  Xujuan Zhou,et al.  A new nested ensemble technique for automated diagnosis of breast cancer , 2020, Pattern Recognit. Lett..

[30]  Ian H. Witten,et al.  Weka-A Machine Learning Workbench for Data Mining , 2005, Data Mining and Knowledge Discovery Handbook.

[31]  Mark A. Hall,et al.  Correlation-based Feature Selection for Machine Learning , 2003 .

[32]  U. Rajendra Acharya,et al.  Automated characterization and classification of coronary artery disease and myocardial infarction by decomposition of ECG signals: A comparative study , 2017, Inf. Sci..

[33]  Kazuyuki Murase,et al.  Adaptive weighted fuzzy rule-based system for the risk level assessment of heart disease , 2018, Applied Intelligence.

[34]  U. Rajendra Acharya,et al.  Novel deep genetic ensemble of classifiers for arrhythmia detection using ECG signals , 2019, Neural Computing and Applications.