Real-time monitoring system for early prediction of heart disease using Internet of Things

The diagnosis of heart disease is found to be a serious concern, so the diagnosis has to be done remotely and regularly to take the prior action. In the present years, the diagnosis of heart disease has become a key research area for researchers and many models have been proposed in recent years. The diagnosis of heart disease can be done using optimization algorithms, and it provides results with good efficiency. The main objective of this paper is to propose a hybrid fuzzy-based decision tree algorithm for the process of prediction of heart disease at an early stage through the continuous and remote patient monitoring system. The results obtained from the proposed algorithm are compared with the various number of classifier algorithms like decision tree J48, naive Bayes, GA with FCM, KNN with NB, ANN, SVM with fuzzy in which the proposed HFDT algorithm provides better accuracy of 98.30%. From the above-obtained results, the proposed hybrid fuzzy-based decision tree algorithm efficiently predicts heart disease compared to the other classifier algorithms in the literature. The proposed work is implemented in the MATLAB environment using the heart disease dataset.

[1]  Sundararaman Gopalan,et al.  IoT Based Low Cost Single Sensor Node Remote Health Monitoring System , 2017, EUSPN/ICTH.

[2]  Arif Gülten,et al.  Classifier ensemble construction with rotation forest to improve medical diagnosis performance of machine learning algorithms , 2011, Comput. Methods Programs Biomed..

[3]  Kemal Polat,et al.  A hybrid approach to medical decision support systems: Combining feature selection, fuzzy weighted pre-processing and AIRS , 2007, Comput. Methods Programs Biomed..

[4]  S. Muthukaruppan,et al.  A hybrid particle swarm optimization based fuzzy expert system for the diagnosis of coronary artery disease , 2012, Expert Syst. Appl..

[5]  M. Kindt,et al.  False Heart Rate Feedback and the Perception of Heart Symptoms in Patients with Congenital Heart Disease and Anxiety , 2009, International journal of behavioral medicine.

[6]  Mu-Yen Chen,et al.  Integrating data mining with case-based reasoning for chronic diseases prognosis and diagnosis , 2007, Expert Syst. Appl..

[7]  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..

[8]  Byoung-Tak Zhang,et al.  AptaCDSS-E: A classifier ensemble-based clinical decision support system for cardiovascular disease level prediction , 2008, Expert Syst. Appl..

[9]  Shantharajah S. Periyasamy,et al.  Optimal body mass index cutoff point for cardiovascular disease and high blood pressure , 2018, Neural Computing and Applications.

[10]  T. Jarin,et al.  Segmentation by Fractional Order Darwinian Particle Swarm Optimization Based Multilevel Thresholding and Improved Lossless Prediction Based Compression Algorithm for Medical Images , 2019, IEEE Access.

[11]  Gunasekaran Manogaran,et al.  Machine Learning Approach-Based Gamma Distribution for Brain Tumor Detection and Data Sample Imbalance Analysis , 2019, IEEE Access.

[12]  C. Nienaber,et al.  Improved Functional Activity of Bone Marrow Derived Circulating Progenitor Cells After Intra Coronary Freshly Isolated Bone Marrow Cells Transplantation in Patients with Ischemic Heart Disease , 2010, Stem Cell Reviews and Reports.

[13]  Shantharajah S. Periyasamy,et al.  An optimized feature selection based on genetic approach and support vector machine for heart disease , 2019, Clust. Comput..

[14]  Carlos Ordonez,et al.  Association rule discovery with the train and test approach for heart disease prediction , 2006, IEEE Transactions on Information Technology in Biomedicine.

[15]  Noor Akhmad Setiawan,et al.  A Comparative Study of Imputation Methods to Predict Missing Attribute Values in Coronary Heart Disease Data Set , 2008 .

[16]  Nabendu Chaki,et al.  Personal Health Record Management System Using Hadoop Framework: An Application for Smarter Health Care , 2016 .

[17]  Jouni Lampinen,et al.  A Classification method based on principal component analysis and differential evolution algorithm applied for prediction diagnosis from clinical EMR heart data sets , 2010 .

[18]  S. K. Srivatsa,et al.  Applying Machine Learning Methods in Diagnosing Heart Disease for Diabetic Patients , 2012 .

[19]  S. P. Shantharajah,et al.  Survey on data analytics techniques in healthcare using IOT platform , 2018 .

[20]  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..

[21]  Dimitrios I. Fotiadis,et al.  Automated Diagnosis of Coronary Artery Disease Based on Data Mining and Fuzzy Modeling , 2008, IEEE Transactions on Information Technology in Biomedicine.

[22]  Paolo Melillo,et al.  Classification Tree for Risk Assessment in Patients Suffering From Congestive Heart Failure via Long-Term Heart Rate Variability , 2013, IEEE Journal of Biomedical and Health Informatics.

[23]  B. S. Saini,et al.  Detection of coronary artery disease by reduced features and extreme learning machine , 2018, Clujul medical.

[24]  R. Varatharajan,et al.  Cloud and IoT based disease prediction and diagnosis system for healthcare using Fuzzy neural classifier , 2018, Future Gener. Comput. Syst..

[25]  Rozaida Ghazali,et al.  The Development of Improved Back-Propagation Neural Networks Algorithm for Predicting Patients with Heart Disease , 2010, ICICA.

[26]  T. V. Padmavathy,et al.  Performance analysis of pre-cancerous mammographic image enhancement feature using non-subsampled shearlet transform , 2018, Multimedia Tools and Applications.

[27]  Gunasekaran Manogaran,et al.  A new architecture of Internet of Things and big data ecosystem for secured smart healthcare monitoring and alerting system , 2017, Future Gener. Comput. Syst..

[28]  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.

[29]  Yi-Ping Phoebe Chen,et al.  Computational intelligence for heart disease diagnosis: A medical knowledge driven approach , 2013, Expert Syst. Appl..

[30]  Min-Soo Kim,et al.  Decision-making model for early diagnosis of congestive heart failure using rough set and decision tree approaches , 2012, J. Biomed. Informatics.

[31]  Mehmet Bayrak,et al.  Assessment of exercise stress testing with artificial neural network in determining coronary artery disease and predicting lesion localization , 2009, Expert Syst. Appl..

[32]  Gholam Ali Montazer,et al.  A fuzzy-evidential hybrid inference engine for coronary heart disease risk assessment , 2010, Expert Syst. Appl..

[33]  G. Usha Devi,et al.  Wireless camera network with enhanced SIFT algorithm for human tracking mechanism , 2018 .

[34]  S. P. Shantharajah,et al.  An optimized feature selection based on genetic approach and support vector machine for heart disease , 2018, Cluster Computing.

[35]  Shantharajah S. Periyasamy,et al.  Remote Health Patient Monitoring System for Early Detection of Heart Disease , 2021, Int. J. Grid High Perform. Comput..

[36]  Gabriele Guidi,et al.  A Machine Learning System to Improve Heart Failure Patient Assistance , 2014, IEEE Journal of Biomedical and Health Informatics.