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IRJET- Disease Prediction and Doctor Recommendation System
1.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 05 Issue: 03 | Mar-2018 www.irjet.net p-ISSN: 2395-0072 © 2018, IRJET | Impact Factor value: 6.171 | ISO 9001:2008 Certified Journal | Page 3207 Disease Prediction and Doctor Recommendation System Dhanashri Gujar1, Rashmi Biyani2, Tejaswini Bramhane3, Snehal Bhosale4, Tejaswita P. Vaidya5 1,2,3,4 B.E. (Computer Engineering), Sinhgad College of Engineering, Pune, Maharashtra, India 5Assistant Professor, Dept. of Computer Engineering, Sinhgad College of Engineering, Pune, Maharashtra, India ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - Properly analyzing clinical documents about patients’ health anticipate the possibility of occurrence of various diseases. In addition, acquiring informationregarding specialists of that particular disease as per the requirement facilitates proper and efficient diagnosis.Thispaperprovidesa novel method that uses data mining technique, namely, Naïve Bayes classification algorithm for prediction of disease followed by recommendation of specialists of the predicted disease. Using medical profiles such as heart rate, blood pressure through sensors and other externally observable symptoms such as fever, cold, headache etc. that patient has, prediction of likelihood of a disease is done. Naïve Bayes algorithm takes these symptoms and predicts disease. Furthermore, all the needful and adequate information regarding the predicted disease as well as the recommended doctors is provided. Recommendation suggests the location, contact and other necessary details of the disease specialists based on the filters chosen by the user out of less fees, more experience, nearest location and feedback reviews of the doctors. Reviews are compared using Stanford’s CoreNLP algorithm. Thus user can get appropriate treatment and necessary medical advice as fast as possible. Additionally, users provide their feedback for the recommended doctors which are then added for analysis in order to make further recommendations based on reviews. Key Words: CoreNLP, Naïve Bayes, Prediction, Recommendation, Weka. 1. Introduction Healthcare industry generates terabytes of data every year. The medical documents maintainedareapoolofinformation regarding patients. The task of extracting usefulinformation or quality healthcare is tricky and important [6]. By analyzing these voluminous data we can predict the occurrence of the disease and safe guard people. Thus, an intelligent system for disease predictionplaysamajorrolein controlling the disease and maintaining the good health status for people by providing accurate and trustworthy disease risk prediction. 1.1 Prediction System In this paper, the focus is on data mining techniques to extract hidden rules and relationships between symptoms and diseases. Disease prediction is done by medical profiles such as blood sugar, blood pressure, blood oxygen,headache and other symptoms. Based on this, the most probable disease is predicted by Naive Bayes classifier. 1.2 Recommendation System In addition to this, the specialists for the predicteddisease are recommended based on filters chosen by user. Review based recommendation is done by fetching the reviews of various doctors provided by the previous users. This is followed by feeding these to CoreNLP for processing. Location based recommendation givesthe nearestspecialist relative to user’scurrent location. Feesbasedandexperience based recommendation consider user’s preferences among various ranges. 2. Motivation At present inordertoremain healthy, regularbodydiagnosis is necessary. Today, there are multiple sources available as individual prediction or recommendation system but the need of the hour is to have an integrated model comprising both. Also, it would be more appropriate and convenient if people could get basic diagnosis online 24x7 rather than visitinghospitals& clinics frequently.Thus,reducingcostand saving time. If certain anomalies found in the diagnosis then recommendationof nearby specialistandhospitalsaccording touser’spreference would facilitatein quickandappropriate treatment. Healthcare being a domain evolving continuously and generating a huge amountof datadevelopsa need to use the data for useful knowledge which attracts large organizations to invest heavily in this field. 3. Related Work Binal T. et al [1], Healthcare decision support system for swine flu prediction using naïve bayes classifier, focuses on the aspect of medical diagnosis by learning patternsthrough the collected data for swine flu using naïve bayes classifier for classifying the patients of swine flu into three categories (least possible, probable or most probable), resultingintoan accuracy of nearly 63.33%. Datasets used for this classification were limited in number. Shengyong W. et al [2], Predicting Disease by using data mining based on healthcare information system, describes the experiments of applying data mining to disease prediction from a large number of real world medical records of hypertension. This paper compares three algorithms– naïve bayes, J-48 and ensemble of five J-48 classifiers. Here, naïve bayes & J-48 showed nearly same accuracy of 83%. Marcelo M. et al [3], A collaborative filteringapproachbased on user’s reviews, proposes a collaborative filtering
2.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 05 Issue: 03 | Mar-2018 www.irjet.net p-ISSN: 2395-0072 © 2018, IRJET | Impact Factor value: 6.171 | ISO 9001:2008 Certified Journal | Page 3208 approach that uses users’ reviews to produce items description. The reviews are processed using CoreNLP tool and then the algorithm creates a representation which is used to compute similarity of items which is used in collaborative filtering approach based on k-nearest neighbors. Manjusha K. et al [5], published Prediction of different dermatological conditions using Naive Bayesian classification, gives the possibilities of eight diseases using patient’s attributes. The system extracts hidden knowledge from the database. System can also predict diseases other than dermatological diseases. 4. Proposed Work An intelligent system for accurate disease prediction and medical facilities recommendation plays a major role in effective treatment. This system takes the symptoms from users and predicts the most accurate disease accordingly. Additionally, sensormodulehelpsincontinuousevaluationof vitals like heart rate, blood pressure and sugar level for patient which is fed in the system at runtime for analysis alongwithother external symptoms.Basedontheprediction, system recommends thehospitals/clinicsaccordingtouser’s preference outof nearest location, lessfess, moreexperience and better reviews with doctors having expertise for that particulardisease to avail the requiredmedications.Also,the users can provide their feedback for the recommended doctors. Figure 1: System Overview Diagram 5. Implementation Techniques 5.1 Naïve Bayes A Naïve Bayesian classifier, is a model joint probability distribution over a set of stochastic variables. Instances of the classification problem under study are presented to the classifier as a combination of valuesfor thefeaturevariables; the classifier then returns a posterior probability distribution over the classvariable. Learningsuchaclassifier amounts to establishing the prior probabilities of the different classes and estimating the conditionalprobabilities of the various features given each of the classes [1]. According to Bayes theorem of probability theory:- (1) It is assumed that attributes E1 to Em are class conditionally independent, which means it is often assumed that (2) After making the above assumption, the classifier is called Naïve Bayes classifier. Table 1: Input Attributes Gender: F/M Blood Sugar: 72-162 mg/dl Blood Pressure: 120/80 mmHg Pulse Rate: 60-100 bpm (normal) Fever: Yes/No Level of Fever: low(1), moderate(2), high(3) Cough, Cold: <7 days =1, >7 days =2 Pain: chest, muscles, body, abdominal, no pain Breathlessness: Yes/No Headache: normal=1, severe=2 Vomiting, Weakness, Chills, Constipation, Dizziness,Loss of Appetite: Yes/No 5.2 Weka Naive Bayes has been implemented using weka libraries. Weka contains a collection of visualization tools and algorithms for data analysis and predictive modeling. It supports several data mining tasks such as data preprocessing, classification, clustering etc. The accuracy, performance measures and confusion matrix for Naive Bayes when applied on training dataset on Weka platform are shown below:
3.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 05 Issue: 03 | Mar-2018 www.irjet.net p-ISSN: 2395-0072 © 2018, IRJET | Impact Factor value: 6.171 | ISO 9001:2008 Certified Journal | Page 3209 Figure 2: Statistics for Naïve Bayes on Weka 5.3 CoreNLP CoreNLP provides a set of natural language analysis tools written in Java for text processing. It takes raw English text input and generates a complete structured analysis of the most common NLP routines. CoreNLP is an integrated framework for several language analysis tools, called annotators [3]. Some of the relevantannotatorscomprisedin this tool are: tokenizer, part-of-speech (POS) tagger, sentence splitter and the sentiment analysis tool. Figure 3: Architecture Diagram 6. Results The most probable disease predicted from the probabilities calculated by the Naïve Bayes classifier with an expected accuracy of above 80 percent. Recommendation of top five specialists corresponding to the predicted diseaseandfilters chosen by user. 7. Conclusion and Future Work Application of data mining techniquesfor diseaseprediction from a large number of real world medical records and CoreNLP techniques for doctor recommendation from the reviews of previous users has been studied and implemented. The system can further be improved by incorporating variousother symptomsand increasing the number of cases for training and testing. Additionally, consideringthecalorie count, step count, sleep quality and other medical profile through wearable, required nutritional diet plan can be suggested. Also, alerts and notifications can be senttimelyto the user as well as his/her guardian in case of any risks. References [1] Binal A. Thakkar, Mosin I. Hasan, Mansi A. Desai, “Healthcare decision support system for swine flu prediction using naïve bayesclassifier”,“IEEE”,101-105, 2010. [2] Feixiang Huang, Shengyong Wang, Chien Chung Chan, “Predicting disease by using data mining based on healthcare information system”, “IEEE”, 2012. [3] Rafael M. D’addio, Marcelo G. Manzato, “A collaborative filtering approach based on users’ reviews”,“IEEE”,204- 209, 2014. [4] F. O. Isinkaye, Y.O. Fola Jimi, B.A. Ojokoh,“ Recmmendation systems : Principles, Methods and Evaluation”, “Elsevier”, 261-273,2015. [5] Manjusha K.K.,K. Sankaranarayanan,SeenaP,“Prediction of different dermatological conditions using naïve Bayesian classification”,”IJARCSSE”,864-868,2014. [6] Subhash C. Pandey, “Data Mining techniquesformedical data: A Review”, “IEEE”, 2016. [7] Lee, Yunkyoung, “Recommendation system using collaborative filtering”, 2015. [8] Geoff A., Elvis C., Mickey E., Sameul S., “Smart wearable body sensorsfor patientsself assistant andmonitoring”, BioMedCentral, 2014. [9] G Gannu, Y Kakodkar, A Marian, “Improving the quality of predictions using textual information in online users reviews”, Information Systems, 2013. [10] Sellappan P., Rafia A., “Intelligent heart disease prediction system using data mining techniques”, IEEE/ACS, 2008.
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