A statistical analysis based recommender model for heart disease patients

OBJECTIVES An intelligent information technology based system could have a positive impact on the life-style of patients suffering from chronic diseases by providing useful health recommendations. In this paper, we have proposed a hybrid model that provides disease prediction and medical recommendations to cardiac patients. The first part aims at implementing a prediction model, that can identify the disease of a patient and classify it into one of the four output classes i.e., non-cardiac chest pain, silent ischemia, angina, and myocardial infarction. Following the disease prediction, the second part of the model provides general medical recommendations to patients. METHODS The recommendations are generated by assessing the severity of clinical features of patients, estimating the risk associated with clinical features and disease, and calculating the probability of occurrence of disease. The purpose of this model is to build an intelligent and adaptive recommender system for heart disease patients. The experiments for the proposed recommender system are conducted on a clinical data set collected and labelled in consultation with medical experts from a known hospital. RESULTS The performance of the proposed prediction model is evaluated using accuracy and kappa statistics as evaluation measures. The medical recommendations are generated based on information collected from a knowledge base created with the help of physicians. The results of the recommendation model are evaluated using confusion matrix and gives an accuracy of 97.8%. CONCLUSION The proposed system exhibits good prediction and recommendation accuracies and promises to be a useful contribution in the field of e-health and medical informatics.

[1]  Parisa Rashidi,et al.  Application of Machine Learning Techniques to High-Dimensional Clinical Data to Forecast Postoperative Complications , 2016, PloS one.

[2]  Ali Idri,et al.  Knowledge discovery in cardiology: A systematic literature review , 2017, Int. J. Medical Informatics.

[3]  Mirza Mansoor Baig,et al.  Smart Health Monitoring Systems: An Overview of Design and Modeling , 2013, Journal of Medical Systems.

[4]  Jae-Kwon Kim,et al.  Adaptive mining prediction model for content recommendation to coronary heart disease patients , 2014, Cluster Computing.

[5]  Saurabh Pal,et al.  Data Mining Approach to Detect Heart Diseases , 2014 .

[6]  Behrouz Minaei,et al.  Dynamic Recommendation: Disease Prediction and Prevention Using Recommender System , 2016 .

[7]  Syed Muhammad Anwar,et al.  Wrapper method for feature selection to classify cardiac arrhythmia , 2017, 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[8]  Jinsul Kim,et al.  An Automated ECG Beat Classification System Using Convolutional Neural Networks , 2016, 2016 6th International Conference on IT Convergence and Security (ICITCS).

[9]  Kyung-Yong Chung,et al.  Knowledge-based dietary nutrition recommendation for obese management , 2016, Inf. Technol. Manag..

[10]  Huan-Chao Keh,et al.  Intelligent Postoperative Morbidity Prediction of Heart Disease Using Artificial Intelligence Techniques , 2012, Journal of Medical Systems.

[11]  Christos Davatzikos,et al.  A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer's disease and its prodromal stages , 2017, NeuroImage.

[12]  Jackson T. Wright,et al.  2014 evidence-based guideline for the management of high blood pressure in adults: report from the panel members appointed to the Eighth Joint National Committee (JNC 8). , 2014, JAMA.

[13]  Ralph B D'Agostino,et al.  Prediction of Lifetime Risk for Cardiovascular Disease by Risk Factor Burden at 50 Years of Age , 2006, Circulation.

[14]  Cheng-Lung Huang,et al.  A GA-based feature selection and parameters optimizationfor support vector machines , 2006, Expert Syst. Appl..

[15]  Chien-Chung Chan,et al.  Predicting disease by using data mining based on healthcare information system , 2012, 2012 IEEE International Conference on Granular Computing.

[16]  Marek Malik,et al.  e-Health: a position statement of the European Society of Cardiology. , 2016, European heart journal.

[17]  Huan Liu,et al.  Feature selection for classification: A review , 2014 .

[18]  D. Levy,et al.  Prediction of coronary heart disease using risk factor categories. , 1998, Circulation.

[19]  George Manis,et al.  Heartbeat Time Series Classification With Support Vector Machines , 2009, IEEE Transactions on Information Technology in Biomedicine.

[20]  Tao Huang,et al.  Promises and Challenges of Big Data Computing in Health Sciences , 2015, Big Data Res..

[21]  Peter C Austin,et al.  Using methods from the data-mining and machine-learning literature for disease classification and prediction: a case study examining classification of heart failure subtypes. , 2013, Journal of clinical epidemiology.

[22]  Moncef Gabbouj,et al.  Real-Time Patient-Specific ECG Classification by 1-D Convolutional Neural Networks , 2016, IEEE Transactions on Biomedical Engineering.

[23]  H. Koh,et al.  Data mining applications in healthcare. , 2005, Journal of healthcare information management : JHIM.

[24]  J. Kai,et al.  Can machine-learning improve cardiovascular risk prediction using routine clinical data? , 2017, PloS one.

[25]  Nilanjan Dey,et al.  Systematic Analysis of Applied Data Mining Based Optimization Algorithms in Clinical Attribute Extraction and Classification for Diagnosis of Cardiac Patients , 2016, Applications of Intelligent Optimization in Biology and Medicine.

[26]  Riccardo Bellazzi,et al.  Predictive data mining in clinical medicine: a focus on selected methods and applications , 2011, WIREs Data Mining Knowl. Discov..

[27]  Nor Ashidi Mat Isa,et al.  Intelligent Medical Disease Diagnosis Using Improved Hybrid Genetic Algorithm - Multilayer Perceptron Network , 2013, Journal of Medical Systems.

[28]  Sijing Zhang,et al.  A framework for massive data transmission in a remote real-time health monitoring system , 2012, 18th International Conference on Automation and Computing (ICAC).

[29]  Bianca Zadrozny,et al.  Information Gain Feature Selection for Multi-Label Classification , 2015, J. Inf. Data Manag..

[30]  Andy Liaw,et al.  Classification and Regression by randomForest , 2007 .

[31]  P. Greenland,et al.  Coronary artery calcium score and risk classification for coronary heart disease prediction. , 2010, JAMA.

[32]  Syed Muhammad Anwar,et al.  Electrocardiogram signal classification to detect arrythmia with improved features , 2017, 2017 IEEE International Conference on Imaging Systems and Techniques (IST).

[33]  Cyril Ferdynus,et al.  A Comparison of a Machine Learning Model with EuroSCORE II in Predicting Mortality after Elective Cardiac Surgery: A Decision Curve Analysis , 2017, PloS one.

[34]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[35]  Maria Lindén,et al.  Machine learning-based clinical decision support system for early diagnosis from real-time physiological data , 2016, 2016 IEEE Region 10 Conference (TENCON).

[36]  Francisco Herrera,et al.  Addressing data complexity for imbalanced data sets: analysis of SMOTE-based oversampling and evolutionary undersampling , 2011, Soft Comput..

[37]  Maryam Farhadian,et al.  Supervised Wavelet Method to Predict Patient Survival from Gene Expression Data , 2014, TheScientificWorldJournal.

[38]  Ji Zhang,et al.  An intelligent recommender system based on predictive analysis in telehealthcare environment , 2016, Web Intell..

[39]  D. Mozaffarian,et al.  Heart disease and stroke statistics--2010 update: a report from the American Heart Association. , 2010, Circulation.

[40]  Ya Zhang,et al.  A machine learning-based framework to identify type 2 diabetes through electronic health records , 2017, Int. J. Medical Informatics.