FOCUS
Real-time monitoring system for early prediction of heart disease using Internet of Things
Shakila Basheer1•Ala Saleh Alluhaidan1•Maryam Aysha Bivi2
Accepted: 3 May 2021 / Published online: 12 May 2021
The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021
Abstract
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 algo- rithms, 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, naı¨ve 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.
Keywords Internet of ThingsSensors: cloud storage Feature selectionClassificationMonitoring system
1 Introduction
In the present year, the usage of the Internet of Things usage is increased day by day. The Internet of Things has been used in a variety of areas like health care, agriculture, industry, and city. Among the other technologies used, radio frequency identification, wireless sensor networks, smart mobile technologies are the major part of usage. IoT with health care provides more suitable, economically, and eco-friendly solutions (Ordonez 2006). The security and privacy feature in the Internet of Things is explained briefly, and the issues present in them are also analyzed. In machine-to-machine network, the communication is between the machines and their advanced technology is the
Internet of Things. IoT provides a wide range of solutions to a different number of areas like health care, industry, city, and agriculture area (Kumar et al.2018). Medical and health care are two different areas, and there is one attractive application for the IoT. The medical devices connected with the sensors are called smart devices.
Internet of Things with health care is found to reduce the cost of the instruments and increases the life span of the people with the help of healthcare providers the machine down-time will be reduced by continuously monitoring them remotely. With the help of IoT, limited resources can be used for efficient scheduling and that can be used for more patients (Huang et al. 2007). Remote patient moni- toring is used to predict the disease at the early stage and diagnosis them, and the patient medical record can be stored in the database for further use. Medical servers and the medical database have the medical record in them so they play a vital role in delivering the health records to the concerned patient. Figure1 represents Healthcare Trends (Nahar et al.2013).
In recent years, health care with IoT field has attracted many researchers to find the ability of the Internet of Things and they analyze various issues happened while doing them practically. The various trends in the healthcare Communicated by Vicente Garcia Diaz.
& Shakila Basheer
sbbasheer@pnu.edu.sa
1 Department of Information Systems, College of Computer and Information Science, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia
2 Department of Computer Science, College of Computer Science, King Khalid University, Abha, Saudi Arabia https://doi.org/10.1007/s00500-021-05865-4(0123456789().,-volV)(0123456789().,- volV)
environment and the various issues present in the health- care environment are addressed to transform the healthcare trends into the Internet of Things innovation to improve the lifetime and machine downtime to overcome the needs of the people (Austin et al. 2013). The contribution is explained below,
(i) The already present IoT-based healthcare is cat- egorized into three types of trends like topology, architecture and platform.
(ii) The existing healthcare service providers and their services and applications are provided.
(iii) The security and privacy issues in the healthcare environment are addressed, and a security model has been suggested.
(iv) To re-shape the healthcare environment to over- come the needs of the people.
The Internet of Things can increase healthcare applica- tions like remote patient monitoring, fitness management, chronic disease, and elderly care and assisted living. The medical devices connected with the sensors are called smart devices. Internet of Things with health care is found to reduce the cost of the instruments and increases the life span of the people with the help of healthcare providers;
the machine down-time will be reduced by continuously monitoring them remotely. With the help of IoT, limited resources can be used for efficient scheduling and that can be used for more patients (Gokulnath and Shantharajah
2019). Remote patient monitoring is used to predict the disease at the early stage and diagnosis them, and the patient medical record can be stored in the database for further use. Medical servers and the medical database have the medical record in them so they play a vital role in delivering the health records to the concerned patient. In recent years, health care with IoT field has attracted many researchers to find the ability of the Internet of Things and they analyze various issues happened while doing them practically. There are many healthcare-based applications, services, and prototypes developed in recent years to overcome the recent issues present in the world (Setiawan et al.2008; Luukka and Lampinen2010; Manogaran et al.
2018a; Babaoglu et al.2009).
2 Related works
In the present years, heart disease is pre-dominantly increasing in humans and from the recent survey, it is found the heart attacks occur mainly because of heart disease (Padmavathy et al.2018). In the developed country, the rate of cardiovascular disease is more. Cardiovascular disease directly attacks the economy and cost of the city or village. The major causes of heart disease are drinking, smoking, hereditary, diabetes, and obesity. Cardiovascular disease can affect newborn babies also checking them is a Fig. 1 Healthcare trends
common thing (Ozcift and Gulten2011). The symptoms of heart disease are fatigue and high chest pain.
The technology improvement in the wireless environ- ment has increased the development of applications in different domains like health care, smart home, smart city, industry, storage, and transport (Babu and Shantharajah 2019). Among this entire domain, the healthcare domain has been growing drastically in the present years, because it can remotely monitor the patients and it can be used in emergencies and the medical data can be stored for future use. The healthcare services can be done for 24 h, and it can also be used in rural areas (Nahar et al.2013).
In the case of the healthcare industry, the wireless body area network plays a vital role in providing the needed services for the healthcare industry, because remote patient monitoring can be done easily using the wireless body area network. The revolution in the healthcare industry cannot be done with only a wireless body area network to make the revolution the advanced technology like the Internet of Things, and cloud computing technology is required (Son and Kim2012).
Internet of Things is used in many healthcare services to provide efficient solutions and to overcome the basic needs of the people. There is no particular protocol or standards for the usage of the Internet of Things, there are some areas where the Internet of Things cannot be used, and it will not produce a desirable solution to overcome the issues (Polat and Gunes2007; Polat et al.2007). A service model has been proposed and that act as a set of solution. The present services and Internet of Things require modifications so that it can be used for healthcare services. The services consist of resource sharing, communication, link protocols, and connectivity. In the present year, the usage of the Internet of Things usage is increased day by day Babu and Shantharajah (2018). The Internet of Things has been used in a variety of areas like health care, agriculture, industry, and city. Among the other technologies used radio fre- quency identification, wireless sensor networks, smart mobile technologies are the major part of usage. IoT with health care provides more suitable, economically, and eco- friendly solutions (Khatibi and Montazer2010). This sec- tion analyzes the present healthcare solutions, and analyzes their architecture, applications, and industry trends in IoT- based health care. The security and privacy feature in the Internet of Things is explained briefly, and the issues pre- sent in them are also analyzed. In machine-to-machine network, the communication is between the machines and their advanced technology is the Internet of Things. IoT provides a wide range of solutions to a different number of areas like health care, industry, city, and agriculture area.
Medical and health care are two different areas, and there is one attractive application for the Internet of Things Muthukaruppan and Er (2012).
3 Internet of Things healthcare services
Internet of Things is used in many healthcare services to provide efficient solutions and to overcome the basic needs of the people. There is no particular protocol or standards for the usage of the Internet of Things, there are some areas where the Internet of Things cannot be used, and it will not produce a desirable solution to overcome the issues (Babu and Shantharajah 2021). A service model has been pro- posed and that act as a set of solution. The present services and Internet of Things require modifications so that can be used for healthcare services. The services consist of resource sharing, communication, link protocols, and connectivity (Markos et al.2008).
3.1 Ambient-assisted living
For example smart home or remote patient, monitoring is used to produce the services to the elderly patients. The Internet of Things with the help of artificial intelligence is used to provide services for elderly patients, and the incapability individuals is known as ambient-assisted liv- ing (Nawi and Ghazali et al.2010). The main objective of ambient-assisted living is to make the elderly patients live their life individually in their place in a safe manner. The service provided by the ambient-assisted living to ensure them they led their life peacefully, and it helps them when there is need of help Eom et al. (2008). Table 1 shows sensors used in healthcare applications.
3.2 Mobile-based Internet of Things
Mobile-based Internet of Things means medical sensors, communicating technologies through mobile or mobile app. The mobile-based Internet of Things provides novel healthcare applications using 4G connectivity and 6 low power wireless sensor network. Mobile-based healthcare services provide a wide range of services like remote patient monitoring, motion monitors, temperature moni- toring (Ahilan et al.2019).
3.3 Adverse drug reaction
The adverse drug reaction is a kind of injury that happens because of taking medicine. This happens because of tak- ing medicine or taking medicine for a long time or taking one or two medicine at the same time. Adverse drug reaction is not generic, and it is not inherited and is not same for all the disease (Turan et al.2011). With the help of IoT-based adverse drug reactions, we can improve the efficiency of the medicine, the Internet of Things can
monitor through the sensor, and it will say when the last medicine was taken.
3.4 Community health care
Community health care is a concept of establishing the coverage area of the Internet of Things over a large area using the local community cloud. This can act as an Internet of Things-based network that can be placed in a residential area, municipal hospital, or in a village (Kars- dorp et al.2009; Manogaran et al.2018b). A special model has been proposed called community health care that helps to collect the requirements at once. The advantage of community health care is to provide health care services in an effective way. Since its network, a suitable authentica- tion and authorization algorithm have to be proposed to make the network a secured one (Garbhapu and Gopalan 2017).
3.5 Children health information
Giving awareness for children’s health and also raising awareness around the public may also increase the health of the children. Awareness is needed for children to over- come their emotional behavior, mental behavior, and their mental health problems. This problem made the researchers to develop a model to overcome this issue called Children Health Information, and this can be performed with the help of the Internet of Things (Melillo et al.2013).
3.6 Wearable device
In recent years, many sensors have been developed for healthcare services and that are designed mainly for the wireless sensor networks-based health care. The same sensors can be used for the same Internet of Things based on healthcare services (Guidi et al. 2014). So there is a necessity of changing the sensor into a useful one so that can be used for healthcare services. The wearable devices can be controlled or monitored through applications or mobile apps. The remote patient monitoring using wearable device or sensor is the main advantage in the healthcare services (Parthiban and Srivatsa 2012; Devi et al. 2018;
Singh et al. 2018). Figure2 represents the IoT healthcare applications.
4 Proposed IoT-based heart disease prediction model
A real-time monitoring system is used in predicting the heart disease, various parameters respiratory rate, oxygen level, temperature level, and blood glucose level fasting and post-meal, and these values are obtained using the wearable sensor attached to the human body; the data are sent and received using the wireless networks and that are stored in the cloud. The proposed framework explains the method that is used in heart disease prediction. The sensors are attached to the patient’s body to detect the presence of Table 1 sensor used in healthcare applications
Infirmity Sensors IOT Connection
Diabetes Opto–physiological sensor IPv6 and 6LoWPAN
Wound analysis Smart phone camera Smart phones system on chip
Heart rate monitor Captive electrodes on circuit board BLE and Wi-Fi
BP monitor Wearable BP monitor sensor WBAN
Body Temperature sensor Wearable body temperature sensor WBAN
Rehabilitation system Wearable sensors and Smart home Heterogeneous wireless connections
Medication management Touch, humidity and co2sensor RFID, wireless links and multimedia transmission Wheelchair management Accelerometer, humidity and gas sensor Smart devises and data-centered layers
Oxygen monitoring Pulse Ox meter Ubiquitous integrated clinical environments
Eye disorder Smart phone camera Software platform in smartphone
Skin infection Smart phone camera Software platform in smart phone
Asthma and chronic disease
Built-in audio microphone system in mobile phone
Software platform in smart phone Cough detection Built-in audio microphone system in mobile
phone
App runs on the software platform in smart phone Melanoma detection Smart phone camera App runs on the software platform in smart phone Remote surgery Surgical robot and sensors Real-time data connectivity and information management
system
disease; the sensors used for prediction are heart rate sen- sors, respiratory rate, and hemoglobin range. The alarming system uses the adaptive alarming system and that alerts message to the respective physician and the care taker. The alert message is sent when the obtained value exceeds the threshold value and the threshold values are set based on the previous method and findings done by the researcher and the government. The threshold model differs for dif- ferent age groups, and it has higher and lower threshold values and so the system monitors continually. Figure3 shows the overview of the proposed system.
4.1 Collection of medical data
The various sensors collect the medical data from the patients using the wearable device placed in their body, and it transfers the data continuously. When the observed value exceeds the threshold value, then the monitoring system records the data and sends the alert message with those clinical values to the doctor and care taker. The proposed framework uses wireless networks to send an alert mes- sage. The algorithm represents the observed clinical values and stores the data in the cloud. Figure4 describes the workflow of the proposed model.
Fig. 2 IoT healthcare applications (Sarkar et al.2016)
4.2 Data preprocessing using R
The data preprocessing is done to remove noisy and redundant data in the dataset, and also it is the process of removing incorrect dataset from the collected original medical datasets. Some of the important methods available in the data preprocessing process are given below:
• To integrate the data from various medical dataset
• Remove the noisy data
• To remove the duplicate data from medical dataset
• Remove the irrelevant data
• To adjust the inconsistency data and To fill the missing data if required
• Reduction of the data
• To identify the outlier data.
4.3 Feature selection
Maximum relevance–minimum redundancy algorithm is used for feature selection in this paper; MRMR extracts the features in the subset which has the strongest correlation with the classification variable. In this algorithm, features with a high correlation are combined even though they are mutually different from each other; MRMR algorithm uses mutual information of a subset between feature and the
class as the relevance of feature for the class. The maxi- mum relevance can be obtained by using the below equation,
Max R X;ð aÞ; R ¼1 X
X
Zi2R
I Zð i:aÞ; ð1Þ I Zð i:aÞdescribes the mutual info between Zi and the class a; MRMR algorithm also uses the mutual information between the features as a redundancy of the feature. The following condition finds the minimum redundancy feature setRD,
Min RD Xð Þ;RD ¼ 1 jX2j
X
Zi;Zj2R
I Z i:Zj
; ð2Þ
I Z i:Zj
represents the mutual information between the two featuresZiandZj:
The above two formula or conditions are combined are called maximum relevance and maximum redundancy. The MRMR has the following formula to optimizeRandRD,
MaxuðR;RDÞ;u¼RDR ð3Þ
where RD-R represents the relevance redundancy of the feature.
Microcontroller Temp- Sensor
BP Sensor
ECG Sensor
BP Sensor
Glucose Sensor
Hemoglobin Sensor
Oxygen Level Sensor
Medical Database
Pre – processing (R)
Feature Extraction (MRMR)
Classifier Module
Disease Prediction
Performance Analysis Fig. 3 Overview of the proposed System
5 Classification using hybrid fuzzy decision tree algorithm
After the feature extraction method, the extracted features are classified using the hybrid fuzzy decision tree algo- rithm. The formation of an expert system involves com- bining huge data obtained from various attributes and the result of feature selection from various factors in knowl- edge. Thus, the design of an algorithm involves a combi- nation of one or more methodologies to form a hybrid methodology. To form this proposed algorithm,
(1) Decision tree algorithm for implementing rule sets and decisions.
(2) Extreme learning machine supervised machine learn- ing algorithm for analytics and handling randomized
data to identify the hidden layer of information called knowledge.
(3) Fuzzy C-means machine learning for its unsuper- vised and categorical fuzzy-based classification methods.
The algorithm acquires the ability of extreme learning machine algorithm to detect hidden values and to identify the ability of the attribute to predict the unknown values using a randomized structure. The ability of fuzzy means to form centroid values and to determine the boundaries of an attribute to contribute to the effective prediction is carried out along with the ELM algorithm. Hence the hybrid fuzzy decision tree algorithm comprises of the following evalu- ation function involved in the derivation.
ð Þ ¼X XL
i¼1
Bi hi xð Þ
ð Þ ð4Þ
arg mincXn
i¼1
Xc
j¼1
wijm xijj cjjj2
ð Þ ð5Þ
Pre – processing (R) Original Feature Dataset
Feature Extraction (MRMR)
Classifier Module
Heart Disease Prediction
Performance Analysis Sensitivity Specificity Accuracy Precision Recall
Fig. 4 Workflow of the proposed model
Table 2 Significant and Non-significant variables
S.No Variable Level Yes No
1 RR Low (\12) 219 96
Medium (12–50) 71 244
High ([50) 35 279
2 HR Low (\60) 101 210
Medium (60–160) 145 168
High ([160) 98 217
3 BP—S Low (\50) 6 309
Medium (50–90) 257 58
High ([90) 55 349
4 BP—D Low (\75) 69 245
Medium (75–140) 103 210
High ([140) 173 467
5 BT Low (\36.6) 70 245
Medium (36.6–37) 212 103
High ([37) 67 249
6 BS—F Low (\70) 157 158
Medium (70–100) 56 259
High ([100) 135 180
7 BS—PM Low (\70) 54 347
Medium (70–140) 47 268
High ([140) 247 78
Combining both these, the new derivation (3) for HFDT is
f Xð Þ ¼arg mincXn
i¼1
Xc
j¼1
wijm xijj cjjj2
ð ÞXL
i¼1
Bi hi xð Þ
ð Þ
ð6Þ where:
c: Centroid value of features.
F(x): Features of the dataset.
Wij: Unknown knowledge.
Xi: Individual feature of dataset.
L: Hidden nodes.
Bi: Resultant expected output.
Hi: Individual hidden node.
n: Nodes.
The algorithm combines the best features of both extreme learning supervised machine learning algorithms as well as fuzzy C-means unsupervised machine learning algorithms, respectively. Hence the resultant algorithm is utilized for the prediction of heart disease. The algorithm is designed with the inputs of both the algorithms, and an array of features is verified for accuracy and other perfor- mance measures. The performance measures include sen- sitivity, specificity. The algorithm design is accomplished with the initialization of variables and features with the definition of the variables used in the algorithm. The algorithm is staged with hybridization of both learners and fuzzy C-means algorithm to improve the accuracy and performance measures as a result of which the reliability of the proposed system.
Fig. 5 heart disease dataset
The novel hybridization of learning with decision tree and fuzzy C-means classification enables the ease of pre- diction of heart disease where a complex mixture of fea- tures is analyzed and results achieved based on input from the user. The initial tested values with clinical attributes proved that this hybridized algorithm would be suitable for accepting and handling more features and result in suc- cessful outcomes.
6 Results and discussions
(HR) and blood pressure (BP)—systolic BP and diastolic BP, are significant variables for the process of prediction of heart disease, and the other variables like body temperature (BT) and blood sugar (BS)—fasting and post-meal, are considered as nonsignificant variables for the process of heart disease prediction. In this study, seven attributes are Table 3 Comparison of
proposed classifier on confusion matrix
Classifier TP TN FP FN Sensitivity Specificity Accuracy
Decision Tree J48 0.83 0.29 0.774 0.83 0.801 0.809 0.91
Naı¨ve Bayes 0.867 0.203 0.836 0.867 0.851 0.904 0.88
GA with FCM 0.72 0.261 0.782 0.782 0.782 0.752 0.81
KNN with NB 0.885 0.246 0.811 0.885 0.846 0.887 0.94
ANN 0.824 0.21 0.824 0.824 0.614 0.894 0.76
SVM with Fuzzy 0.98 0.18 0.98 0.97 0.97 0.9 0.89
HFDT 0.944 0.123 0.97 0.96 0.76 0.919 0.98
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Decision
Tree J48 Naïve Bayes GA with
FCM KNN with
NB ANN SVM with
Fuzzy HFDT
Confusion Matrix
Classifier Performance Measure
TP TN FP FN Sensivity Specificity Accuracy
Fig. 6 comparison of proposed classifier with confusion matrix
Table 4 Comparisons with the proposed classifier with the existing methods
S.No Classification algorithm No. of Correctly Classified Instances No. of Wrongly Classified Instances Accuracy
1 Decision Tree J48 83.70 16.30 83.70
2 Naı¨ve Bayes 76.67 23.33 76.14
3 GA with FCM 75.19 24.81 75.66
4 KNN with NB 78.15 21.85 78.18
5 ANN 74.44 25.56 74.70
6 SVM with Fuzzy 83.33 16.67 84.30
7 HFDT 98.15 1.85 98.30
used for performing the experiment and Table2represents the significant and nonsignificant variables.
7 Performance evaluation
The validations metric is defined by the following param- eters and their equations are stated below which evaluates the performance and forecasting model. The overall per- formance of the proposed method is evaluated using vari- ous parameters, namely sensitivity, specificity, accuracy, precision, and recall. Figure5 shows the attributes in the heart disease dataset.
Sensitivity¼ TP
TPþFN ð7Þ
Specificity¼ TN
TNþFP ð8Þ
The accuracy of the proposed model is determined by evaluating the correctness of the significant variables.
Accuracy¼ TPþTN TNþTPþFPþFN
ð Þ ð9Þ
Precision is the measure of the positive predicted value that is relevant to accurate segmented output. The recall is yet another parameter that is used to check the relevancy in the results and Table3shows the comparison of proposed classifier on confusion matrix, and Fig.6 shows compar- ison of proposed classifier on confusion matrix,
Precision¼ TN
TPþFP ð10Þ
Recall¼ TN
TPþFN ð11Þ
The proposed technique is compared with other existing techniques available in the literature in terms of correctly and wrongly classified instances. Table 4depicts compar- isons with the proposed classifier with the existing meth- ods, Fig.7shows the comparison of a number of correctly and wrongly classified instances, and Fig.8 shows the classification accuracy.
Fig. 7 comparison of number of correctly and wrongly classified Instance
Fig. 8 Comparison of accuracy
8 Conclusion
The prediction of diseases of the heart is intended to help cardiologists in diagnosis. This method is proposed for classifying the data on ailments in the heart. The medical history of the patients and symptoms will have a major trait of selecting the method used for dataset plummeting. If unrelated and redundant structures are removed from the data, the choice of the structure will help in the enhance- ment of the presenting of the models if the decreased data go for its classification. The evaluation metrics used to evaluate the proposed technique consist of sensitivity, specificity, and accuracy. Overall, the proposed technique could achieve sensitivity, specificity, and accuracy values of 0.76, 0.91, and 0.98, respectively. From the provided results, the proposed method was able to segment the MR images affected with tumor more efficiently and accurately compared to other existing segmentation techniques available in the literature.
Acknowledgements This research was funded by the Deanship of Scientific Research at Princess Nourah bint Abdulrahman Universiry through the Fast-track Research Funding Program.
Declarations
Conflict of interest The authors declare that they have no conflict of interest.
References
Ahilan A, Manogaran G, Raja C, Kadry S, Kumar SN, Kumar CA, ...
Murugan NS (2019) Segmentation by fractional order darwinian particle swarm optimization based multilevel thresholding and improved lossless prediction based compression algorithm for medical images.IEEE Access,7, 89570-89580
Austin PC, Tu JV et al (2013) Using methods from the data-mining and machine-learning literature for disease classification and prediction: a case study examining classification of heart failure subtypes. J Clinic Epidemiol 66(4):398–407
Babaoglu I, Baykan OK, Aygul N, Ozdemir K, Bayrak M (2009) Assessment of exercise stress testing with artificial neural network in determining coronary artery disease and predicting lesion localization. Exp Syst Appl 36(2):2562–2566
Babu GC, Shantharajah SP (2018) Survey on data analytics techniques in healthcare using IOT platform. Int J Reason-Based Intell Syst 10(3–4):183–196
Babu GC, Shantharajah SP (2021) Remote health patient monitoring system for early detection of heart disease. Int J Grid High Perform Comput (IJGHPC) 13(2):118–130
Babu GC, Shantharajah SP (2019) Optimal body mass index cutoff point for cardiovascular disease and high blood pressure. Neural Comput Appl 31(5):1585–1594
Devi GU, Priyan MK, Gokulnath C (2018) Wireless camera network with enhanced SIFT algorithm for human tracking mechanism.
Int J Internet Tech Secured Trans 8(2):185–194
Eom JH, Kim SC et al (2008) AptaCDSS-E: A classifier ensemble- based clinical decision support system for cardiovascular disease level prediction. Exp Syst Appl 34(4):2479–2008
Garbhapu VV, Gopalan S (2017) IoT based low cost single sensor node remote health monitoring system. Procedia Comput Sci 113:408–415
Gokulnath CB, Shantharajah SP (2019) An optimized feature selection based on genetic approach and support vector machine for heart disease. Clust Comput 22(6):14777–14787
Guidi G, Pettenati MC, Melillo P, Iadanza E (2014) A machine learning system to improve heart failure patient Assistance.
IEEE J Biomed Health Inf 18(6):1750–1756
Huang MJ, Chen MY, Lee SC (2007) Integrating data mining with case-based reasoning for chronic diseases prognosis and diag- nosis. Exp Syst Appl 32(3):856–867
Karsdorp PA, Kindt M, Rietveld S, Everaerd W, Mulder BJ (2009) False heart rate feedback and the perception of heart symptoms in patients with congenital heart disease and anxiety. Int J Behav Med 16(1):81–88
Khatibi V, Montazer GA (2010) A fuzzy-evidential hybrid inference engine for coronary heart disease risk assessment. J Exp Syst Appl 37:8536–8542
Kumar PM, Lokesh S, Varatharajan R, Babu GC, Parthasarathy P (2018) Cloud and IoT based disease prediction and diagnosis system for healthcare using Fuzzy neural classifier. Futur Gener Comput Syst 86:527–534
Luukka P, Lampinen J (2010) A Classification method based on principal component analysis and differential evolution algo- rithm applied for prediction diagnosis from clinical EMR heart data sets. Computational Intelligence in Optimization. Springer, Berlin, pp 263–283
Manogaran G, Shakeel PM, Hassanein AS, Kumar PM, Babu GC (2018a) Machine learning approach-based gamma distribution for brain tumor detection and data sample imbalance analysis.
IEEE Access 7:12–19
Manogaran G, Varatharajan R, Lopez D, Kumar PM, Sundarasekar R, Thota C (2018b) A new architecture of Internet of Things and big data ecosystem for secured smart healthcare monitoring and alerting system. Future Gen Comput Syst 82:375–387
Markos GT, Themis PE et al (2008) Automated diagnosis of coronary artery disease based on data mining and fuzzy modeling. J IEEE Trans Inform Tech Biomed 12(4)
Melillo P, Luca ND, Bracale M, Pecchia L (2013) Classification tree for risk assessment in patients suffering from congestive heart failure via long-term heart rate variability. IEEE J Biomed Health Inf 17(3):727–733
Muthukaruppan S, Er MJ (2012) A hybrid particle swarm optimiza- tion based fuzzy expert system for the diagnosis of coronary artery disease. J Exp Syst Appl 39:11657–11665
Nahar J, Imam T et al (2013) Association rule mining to detect factors which contribute to heart disease in males and females. Expert Syst Appl 40(4):1086–1093
Nahar J, Imam T, Tickle KS, Chen YPP (2013) Computational intelligence for heart disease diagnosis: a medical knowledge driven approach. Expert Syst Appl 40(1):96–104
Nawi NM, Ghazali R et al (2010) The development of improved back-propagation neural networks algorithm for predicting patients with heart disease. International conference on infor- mation computing and applications. Springer, Berlin, pp 317–324
Ordonez C (2006) Association rule discovery with the train and test approach for heart disease prediction. IEEE Trans Inform Tech Biomed 10(2):334–343
Ozcift A, Gulten A (2011) Classifier ensemble construction with rotation forest to improve medical diagnosis performance of
machine learning algorithms. Comput Methods Prog Biomed 104(3):443–451
Padmavathy TV, Vimalkumar MN, Nagarajan S, Babu GC, Parthasarathy P (2018) Performance analysis of pre-cancerous mammographic image enhancement feature using non-subsam- pled shearlet transform. Multimedia Tools Appl 1–1
Parthiban G, Srivatsa SK (2012) Applying machine learning methods in diagnosing heart disease for diabetic patients. Int J Appl Inf Syst 3(7):25–30
Polat K, Gu¨nes¸ S (2007) A hybrid approach to medical decision support systems: Combining feature selection, fuzzy weighted pre-processing and AIRS. Comput Methods Prog Biomed 88(2):164–174
Polat K, S¸ahan S, Gu¨nes¸ S (2007) Automatic detection of heart disease using an artificial immune recognition system (AIRS) with fuzzy resource allocation mechanism and k-nn (nearest neighbour) based weighting preprocessing. Exp Syst Appl 32(2):625–631
Sarkar BB, Paul S, Cornel B, Rohatinovici N, Chaki N (2016) Personal health record management system using Hadoop framework: an application for smarter health care. International
workshop soft computing applications. Springer, Berlin, pp 385–393
Setiawan NA et al (2008) A comparative study of imputation methods to predict missing attribute values in coronary heart disease data set.J Dep Elect Electron Eng 21, 266–269
Singh RS, Saini BS, Sunkaria RK (2018) Detection of coronary artery disease by reduced features and extreme learning machine.
Clujul Med 91(2):166
Son C-S, Kim Y-N et al (2012) Decision-making model for early diagnosis of congestive heart failure using rough set and decision tree approaches. J Biomed Inform 45:999–1008
Turan RG, Bozdag-t I et al (2011) 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. Stem Cell Rev Rep 7(3):646–656
Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
