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IRJET- Patient Health Monitoring using IoT with Machine Learning
1.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 03 | Mar 2019 www.irjet.net p-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 7514 Patient health monitoring using IoT with machine learning D.M. Jeya Priyadharsan1, K. Kabin Sanjay2, S. Kathiresan3, K. Kiran Karthik4, K. Siva Prasath5 jpdharsan@gmail.comElectronics and Communication Engineering, Sri Shakthi Institute of Engineering and Technology, Coimbatore, Anna University, Chennai, Tamilnadu, India. kabinsanjay7997@gmail.comElectronics and Communication Engineering, Sri Shakthi Institute of Engineering and Technology, Coimbatore, Anna University, Chennai, Tamilnadu, India. skathirece2@gmail.comElectronics and Communication Engineering, Sri Shakthi Institute of Engineering and Technology, Coimbatore, Anna University, Chennai, Tamilnadu, India. kirankarthik7071@gmail.com Electronics and Communication Engineering, Sri Shakthi Institute of Engineering and Technology, Coimbatore, Anna University, Chennai, Tamilnadu, India. ksivaprasath@siet.ac.in Assistant Professor, Electronics and Communication Engineering, Sri Shakthi Institute of Engineering and Technology,Coimbatore,Anna University, Chennai, Tamilnadu, , India. ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - Health aspects of human being need to be monitored with utmost care and must be treated with appropriate drugs. Several diseases can be reduced by proactive monitoring of one’s health. In the recent decades, technological development is at its peak due to which several wearable devices and health monitoringgadgetsareavailable at the market. Even expert doctors find it challenging to estimate the health issues from the symptoms observed from the diseased. Using modern technological tools such as Internet of Things (IoT), machine learning and Artificial Intelligence along with Big data makes the job of physicians much easier in digging out the root cause of disease and predicting its seriousness using modern algorithms. In this research work, the machine learning algorithms are used to monitor the health conditions of the humans. Initial training and validation of machine learning algorithms are performed using the UCI dataset. Testing phase is carried out by collecting heart rate, blood pressure and temperature of the person using IoT setup. Testing phase estimatestheprediction of any abnormalities in the health condition from the sensor data collected through the IoT framework. Statisticalanalysis is performed from data accumulated into the cloud from IoT device to estimate the accuracy in predictionpercentage. Also, from the results obtained from the K-Nearest Neighbor outperforms other conventional classifiers. Key Words: Raspberry Pi, Cloud, IoT 1.INTRODUCTION The essential part of the life is healthcare. Health care is the maintenance and improvement of health via prevention and diagnosis of diseases. Any ruptures or abnormalities that are present deep beneath the skin can be diagnosed with the help of the diagnostic equipment like CT, MRI, PET, SPECT etc. Also, certain abnormal conditions like heart attack, epilepsy can be detected even before they occur The steady increase in the population and also a unpredictable spread of chronic illness among masses have created a strain on modern health care systems [1], and the demand for resources from hospital beds to doctors andnursesisextremelyhigh[2].Obviously,a solution is required to reduce the pressure on healthcare systems while themaintainingthestandard and quality of the health care provided at its optimum level. The Internet of Things (IoT) is a potential solution to curb the pressures on healthcare systems, and has thus been the focus of much recent research [3–7].the occasional monitoring of patients with diabetics is detailed in [5] and also monitoring of patients with specific disease such as Perkinson disease is described in [6]. Further research looks to serve specific purposes, such as aiding rehabilitation through constant monitoring of a patient’s progress [7]. Emergency healthcare has also been identifiedasa possibility by related works [8, 9], but has not yet been widely researched. Several related works have previously surveyed specific areas and technologies related to IoT healthcare. An extensive survey is presented in [10], with focus placed on commercially available solutions, possible applications, and
2.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 03 | Mar 2019 www.irjet.net p-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 7515 remaining problems. Each topic is considered separately, rather than as part of an overarching system. In [11], data mining, storage, and analysis are considered, with little mention of integration of these into a system. RFIDs were used in American hospitals to advance care and to reduce the costs of treatment [12]. The cardiac impulses of the patient can be monitored by the doctor using healthcare monitoring system and, it helps the doctor to provide proper diagnosis. A number of wearable systems have been proposed to provide reliable wireless transmission of data [13]. Although there are many benefits of the internet of things in health care, they are not without any challenges. Both the hospital executives and IT are concerned about data security and IoT device management [14]. MachineLearningisafieldwhichiselevatedout of Artificial Intelligence (AI). Applying artificial intelligence, we can build better and shrewd machines. Machine Learning is aplantogainfromprecedentsand experience, without being expressly modified. Instead of writing code, the data is fed to the generic algorithm, and logic is built based on the data given. Machine learning is used in web searches, spam filtering, ad placement, stock trading and so on [15]. Machine Learning gains equal importance and recognition as that of bigdataandcloudcomputingbyanalyzingthose big chunks and simplifying the task of data scientists in an automated process. Expansive scale informational collectionsaregatheredandexaminedinvariousareas, fromdesigningsciencestointerpersonalorganizations, business, bio molecular research, and security [16]. Most traditional machine learning algorithms are intended for data that would be entirely loaded into memory [17]. Even though learning from these plentiful data is expected to bring substantial science and engineering advances along with enhancements in quality of our life [18], it brings tremendous encounters at the same time. Machine learning techniques have been widely adopted in a number of massive and complex data-intensive fields such as medicine, astronomy, biology, and so on, for these techniques provide possible solutions to mine the information hidden in the data [19]. A report from McKinsey Global Institute proclaims that machine learning will be the driver of the subsequent big wave of innovation [20]. 2.IOT ARCHITECTURE FOR DISEASE DETECTION Thissystemprovidesaplatformformonitoringand sup ervising patients by the usage sensor networks. The design consists of both hardware and software sections. The hardware section includes Heart rate sensor, Temperature sensor, Blood Pressure sensor and Raspberry Pi board. The stages of the process are as follows: collection of sensor values, storage of data in the cloud and the analysis of the data stored in the cloud to check for abnormalities in the health condition. The abnormalities usually occur when there is an unrecognized activity in the body parts. The occurrence of seizures in the brain can increase the rate of heartbeat. The heart rate can be measured by using heart rate sensor. The rate of the heart at every instant can be measured. The sensor is interfaced with Raspberry Pi board in order to visualize the resultant values. The values can be visualized by using either serial monitor or by interfacing an LCD display. Since Fig. 1. Block Diagram
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International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 03 | Mar 2019 www.irjet.net p-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 7516 the volume of data is large, all the data collected are sent to the cloud. The data send to the cloud are analyzed in the local side. In general, the rate of heart beat tends to increase gradually for any abnormal conditions. Several open source cloud platforms support Raspberry Pi board and Raspbian jessi is one among them. It is an open source platform and the data collected are uploaded in the cloud by registering the physical address. The analysis of the values collected for the presence of any abnormalities is done by using machine learning algorithms. The values collected in the cloud are thenimportedforthepurposeofanalysis. The system should be trained using sample values for making predictions. In order to get accurate predictions, large amount data should be trained. Larger the data trained, higher the accuracy. The dataset that is to be trained should consist of the data collected from multiple persons under multiple circumstances. Also, the dataset should have the data collected from persons of different age and it should possess the data of both healthy and unhealthy persons. The data that is tested till the previous session should be included in the training dataset and the data that is collected at the instantaneous momentisthedatatobe tested. By the usage of the data trained initiallyandthe practical data that is included, prediction is made. 3.PROPOSED SYSTEM The proposed system is that the data from the sensor network are collected and processed by the microntroller. The proposed results are stored in the cloud. From the cloud the processed data can be retrieved for analysis. The analyzed data is again stored in the cloud which can be retrieved by the doctors. The values obtained and the state of the person is made available in the hospital webpage. The block diagram of the entire setup is depicted in Fig 2. The entire system can be sectored into three major parts. Health monitoring system, Health state predictionsystem and Emergency alert system are the three major sectors of theproposal.Sincetheapproach is dealing with health related issues, the data collected and processes has to be kept confidentially. To ensure confidentiality and security, encryption mechanisms are used which adds credit to the drafted system. Fig 2: Block diagram The health monitoring module comprises of the hardware components of the system that makes it IoT enabled and is used to record the health parameters of the patient using various sensors. Here, Raspberry pi acts as a central server to which all the sensors are connected through the GPIO pins or using MCP3008 analog-to-digital convertor if their output is in the analog form as Raspberry pi works only on digital signals. Health state prediction is one of the most promising modules of the proposed system. In this module, the patients’ healthdataarecollectedfromthe sensory nodes and stored in the database. The data stored in the database is subjected to be tested in the KNN classifier which classifies various states of the person’s health. The accurate classification is made by the classifier which hardly needs manual rechecking.
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International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 03 | Mar 2019 www.irjet.net p-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 7517 The concept of including machine learning algorithm drives the scope of this module to a larger extent. Machine learning algorithms helps the proposed system to mimic the potential of scholarly doctors in terms of disease prediction data mining techniques do justice to the prediction methodology. 4.MACHINE LEARNING ALGORITHMS FOR HEALTH STATE PREDICTION Analysis of the dataset can be done by using machine learning approach. A classification technique (or classifier) is a systematic approach for building classification models from an input data set. Few examples for machine learning algorithms include decision tree classifiers, rule-based classifiers, adaboost classifiers, neural networks, support vector machines, least squares regression, Knn classifier and Naive Bayes classifiers. Each technique provides a learning algorithm to recognize a model that best fits the bond between the attribute set and class label of the given input data. The model generated by a learning algorithm should be able to fit the input data well and also correctly foretell the class labels of records it has never seen before. The key objective of each learning algorithms is to build models with good generalization capability. Using the machine learning algorithms, the dataset forhealthmonitoringistrained and analysis is done based on the training. The classifier used in this approach is K-Nearest Neighbor classifier A. K-Nearest Neighbour (KNN) classifier: Knn is a non-parametricsupervisedlearningtechnique in which the data is classified to a given category with the help of training set. Predictions are made for a new instance (x) by searching through the entire training set for the K most similar cases (neighbors) and summarizing the output variables for those K cases. In the classification, this is the mode class value. Its purpose is to use a database in which the data points are separated into several classes to predict the classification of a new sample point. The steps of classification are as follows: 1. Training phase: a model is constructed from the training instances. The classification algorithm finds relationships between predictors and targets. The relationships are summarized in a model. 2. Testing phase: test the model on a test sample whose class labels are known but not used for training the model. 3. Usagephase:usethemodelforclassificationon new data whose class labels are unknown. The flow of the Knn classifier is shown in Fig 3. Fig 3: Flow of the classifier B. K- Nearest neighbour algorithm: The algorithm of Knn classifier is explained with an example. Consider Fig 4, there are two different target classes, white and orange circles. Totally there are 26 training samples. The prediction has to be done for blue circles. ConsideringKvalueasthree,thesimilarity in the distance is calculated using similarity measures like Euclidean distance. Initialization, Defined K Computing distance (test instance, train instance Sort the distance Take K Nearest neighbors Apply simple majority Classe s
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International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 03 | Mar 2019 www.irjet.net p-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 7518 Fig 4: sample daigram If the similarity score is less which means the classes are close. From the image, the distance is calculated and less distance circlesareplacedinsidetheBigcircle. Let’s consider a setup with “n” trainingsamples,where xi is the trainingdatapoint.Thetrainingdatapointsare categorized into “c” classes. Using KNN, the aim is to predict class for the new data point. So, the first step is to calculate the distance (Euclidean) between the new data point and all the training data points. To arrange all the distances in non-decreasing order. Assuming a positive value of “K” and filtering “K” least values from the sortedlist.Now,theKtopdistancesare available. Nearest neighbor is a special case of k-nearest neighbor class. Where k value is 1 (k = 1). In this case, new data point target class will be assigned to the 1st closest neighbor. Selecting the value of K in K-nearest neighbor is the most critical problem. A small value of K means that noise will have a higher influence on the result i.e., the probability of over fitting is very high.Alargevalue of K makes it computationally expensive and defeats the basic idea behind KNN (that points that are near mighthavesimilar classes).Asimpleapproachtoselect k is k = n^ (1/2). To optimize the results, we can use Cross Validation. Using the cross-validation technique, we can test KNN algorithm with different values of K. The model which gives good accuracy can beconsideredtobeanoptimal choice. It depends on individual cases, at times best process is to run through each possible value of k and test our result. The classification techniques can be classified into the following three groups: 1. Parametric 2. Semi parametric 3. Non-Parametric Parametric & Semi parametric classifiers need specific information about the structure of data in training set. It is difficult to fulfill this requirement in many cases. So, non-parametric classifier like KNNwasconsidered. Data was randomly split into training, cross-validation & testing data. Experimentation was done with the value of K from K = 1 to 15. 98.02%. The best performance was obtained when K is 1. C. AdvantagesofK-nearestneighboursalgorithm: Knn is simple to implement. Knn executes quickly for small training data sets. Performance asymptotically approaches the performance of the Bayes Classifier. Don’t need any prior knowledge about the structure of data in the training set. No retraining is required if the new training pattern is added to the existing training set. D. Limitation to K-nearest neighbours algorithm: When the training set is large, it may take a lot of space. For every test data, the distance should be computed between test data and all the training data. Thus a lot of time may be needed for the testing.
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International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 03 | Mar 2019 www.irjet.net p-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 7519 PERFORMANCE ANALYSIS & DISCUSSION The performance of various machine learning algorithms depends on the nature of the dataset. The given dataset is analyzed using three algorithms viz., decision tree algorithm, naïve bayes algorithm and support vector machine algorithm. As per the analysis represented in Fig., it is observed that the accuracy is greater for decision tree algorithm than the others. Accuracyis the ratio of number of accuratepredictions of the total number of the given input samples. Several major factors affect the accuracy of algorithm and mean is one among those factors. If any value is found inappropriate in the dataset, those values can be replaced by the mean values. Mean is inversely proportional to accuracy. In order to get increased accuracy, mean value should be low. Fig. shows the relation between accuracy and mean. Also, the nature of dataset is more reliable with the decision tree algorithm which makes the accuracy higher. The accuracy of decision tree algorithm is obtained as 76.4% which is 5.3% greater than naïve bayes algorithm and 7.83 % greater than support vector machine. 5. CONCLUSIONS Health factors of human if left unnoticed will result in serious issues and even cause danger to their life. Automating the continuous monitoring of heath parameters through IoT is discussed as novel solution. Technology plays the major role inhealthcarenotonly for sensory devices but also in communication, recording and display device. It is very important to monitor various medical parameter sand post operational days. Hence the latest trend in Healthcare communication method using IOT and Machine learning techniques. Internet of things serves as a catalyst for the health care and plays prominentrolein wide range of health care applications. Initial training and validation of machine learning algorithms are performed using the UCI dataset. Testing phase estimates the prediction of abnormalities from the sensor data collected through the IoT framework. Statisticalanalysisisperformedfromdataaccumulated into the cloud from IoT device to estimate theaccuracy in prediction percentage. For this kind of IoT platform based continuous monitoring of human heath parameters, machine learning algorithms hadplayeda significant role. 6.REFERENCES [1] AUSTRALIAN INSTITUTE OF HEALTH AND WELFARE, “AUSTRALIA’S HEALTH,” 2014. [2] E. PERRIER, POSITIVE DISRUPTION: HEALTHCARE, AGEING & PARTICIPATION IN THE AGE OF TECHNOLOGY. AUSTRALIA: THE MCKELL INSTITUTE, 2015. [3] P. GOPE AND T. HWANG, “BSN-CARE: A SECURE IOTBASED MODERN HEALTHCARE SYSTEM USING BODY SENSOR NETWORK,” IEEE SENSORS JOURNAL, VOL. 16, NO. 5, PP. 1368–1376, 2016. [4] N. ZHU, T. DIETHE, M. CAMPLANI, L. TAO, A. BURROWS,N.TWOMEY,D.KALESHI,M.MIRMEHDI,P. FLACH, AND I. CRADDOCK, “BRIDGING E-HEALTH AND THE INTERNET OF THINGS: THE SPHERE PROJECT,” IEEE INTELLIGENT SYSTEMS, VOL. 30, NO. 4, PP. 39– 46, 2015. [5] S. H. CHANG, R. D. CHIANG, S. J. WU, AND W. T. CHANG, “A CONTEXT-AWARE, INTERACTIVE M- HEALTH SYSTEM FOR DIABETICS,” IT PROFESSIONAL, VOL. 18, NO. 3, PP. 14–22, 2016. [6] C. F. PASLUOSTA, H. GASSNER, J. WINKLER, J. KLUCKEN,AND B.M.ESKOfiER,“AN EMERGING ERA IN THE MANAGEMENT OF PARKINSON’S DISEASE: Fig 5: Performance of various machine learning algorithms for Epilepsy detection using train and test ratio of 8:2
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International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 03 | Mar 2019 www.irjet.net p-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 7520 WEARABLE TECHNOLOGIES AND THE INTERNET OF THINGS,”IEEEJOURNAL OF BIOMEDICALANDHEALTH INFORMATICS,VOL.19,NO.6,PP.1873–1881,2015. [7] Y. J. FAN, Y. H. YIN, L. D. XU, Y. ZENG, AND F. WU, “IOTBASED SMART REHABILITATION SYSTEM,” IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, VOL. 10, NO. 2, PP. 1568–1577, 2014. [8] S. SARKAR AND S. MISRA, “FROM MICRO TO NANO: THE EVOLUTION OF WIRELESS SENSOR-BASED HEALTH CARE,”IEEEPULSE,VOL.7,NO.1,PP.21–25, 2016. [9] Y. YIN, Y. ZENG, X. CHEN, AND Y. FAN, “THE INTERNET OF THINGS INHEALTHCARE:ANOVERVIEW,” JOURNAL OF INDUSTRIALINFORMATIONINTEGRATION, VOL. 1, PP. 3–13, 3 2016. [10] S.M.R.ISLAM,D.KWAK,H.KABIR,M.HOSSAIN,AND K.-S. KWAK,“THE INTERNET OF THINGS FOR HEALTH CARE: A COMPREHENSIVE SURVEY,” IEEE ACCESS, VOL. 3, PP. 678 – 708, 2015. [11] D.V.DIMITROV,“MEDICALINTERNETOFTHINGSAND BIG DATA IN HEALTHCARE,” HEALTHCARE INFORMATICS RESEARCH, VOL. 22, NO. 3, PP. 156– 163,72016.Z.WEI,W.CHAOWEI,ANDY.NAKAHIRA, "MEDICAL APPLICATION ON INTERNET OF THINGS," IN COMMUNICATION TECHNOLOGY AND APPLICATION, IETINTERNATIONALCONFERENCEON,2011,PP.660- 665, 2011. [12] LO, B.P., THIEMJARUS, S., KING, R., YANG, G.-Z (2005) BODY SENSOR NETWORK—A WIRELESS SENSOR PLATFORM FOR PERVASIVE HEALTHCARE MONITORING. [13] ALA AL-FUQAHA, MOHSEN GUIZANI, MEHDI MOHAMMADI, MOHAMMED ALEDHARI, MOUSSAAYYASH, INTERNET OF THINGS: A SURVEY ON ENABLING TECHNOLOGIES, PROTOCOLS AND APPLICATIONS. IEEE COMMUNICATIONS SURVEYS & TUTORIALS (VOLUME:17, ISSUE:4 ,FOURTHQUARTER 2015). [14] PEDRO DOMINGOS, A FEW USEFUL THINGS TO KNOW ABOUT MACHINE LEARNING.COMMUNICATIONSOFTHE ACM, VOLUME 55 ISSUE 10, OCTOBER 2012, PAGES 78-87. [15] A SANDRYHAILA, JMF MOURA, BIG DATA ANALYSIS WITH SIGNAL PROCESSING ON GRAPHS: REPRESENTATION AND PROCESSING OF MASSIVE DATA SETS WITH IRREGULAR STRUCTURE. IEEE SIGNAL PROC MAG 31(5), 80–90 (2014) [16] J GANTZ, D REINSEL, EXTRACTING VALUE FROM CHAOS (EMC, HOPKINTON, 2011). [17] KSLAVAKIS, GBGIANNAKIS, GMATEOS,MODELING AND OPTIMIZATION FOR BIG DATA ANALYTICS: (STATISTICAL) LEARNING TOOLS FOR OUR ERA OF DATA DELUGE.IEEESIGNAL PROC MAG 31(5),18–31(2014). [18] JUNFEI QIU, QIHUI WU, GUORU DING, YUHUA XU, SHUO FENG, A SURVEY OF MACHINE LEARNING FOR BIG DATA PROCESSING.EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING. [19]J.MANYIKA,M.CHUI,B.BROWN,J.BUGHIN,R.DOBBS, C. ROXBURGH, AND A. BYERS. BIG DATA: THE NEXT FRONTIER FOR INNOVATION, COMPETITION, AND PRODUCTIVITY. TECHNICAL REPORT, MCKINSEY GLOBAL INSTITUTE, 2011. [20]JANMENJOYNAYAK,BIGHNARAJNAIK, H.S.BEHERA, A COMPREHENSIVE SURVEY ON SUPPORT VECTOR MACHINE IN DATA MINING TASKS, INTERNATIONAL JOURNAL OF DATABASE THEORY AND APPLICATION, VOL.8. NO.1 (2015).
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