Care expert assistant for Medicare system using Machine learning

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056

Volume: 09 Issue: 12 | Dec 2022 www.irjet.net p-ISSN: 2395-0072

Care expert assistant for Medicare system using Machine learning

SNEHAL BHOIR 1 , PRITI DESHMUKH 2 , PRAJAKTA JADHAV 3 , MOHINI WAGH 4

1Snehal Bhoir, Dept. of Computer Engineering, Shatabdi Institute of Engineering & Research, Maharashtra, India.

2Priti Deshmukh, Dept. of Computer Engineering, Shatabdi Institute of Engineering & Research, Maharashtra, India.

3Prajakta Jadhav, Dept. of Computer Engineering, Shatabdi Institute of Engineering & Research, Maharashtra, India.

4Mohini Wagh, Dept. of Computer Engineering, Shatabdi Institute of Engineering & Research, Maharashtra, India. ***

Abstract - This project Care assistant for health care system includes registration of patients, storing their detailsintothesystem,andadditionallyprocessedrequest within the pharmacy, and labs. The software package has the ability to abandon a novel id for each patient and storestheclinicaldetailsofeachpatientandhospitaltests done mechanically. It provides a groundwork facility for understandingofeverypatient.Userswillsearchdetailsof a patient mistreatment the id. The prediction assistant which is knowledgeable for health care system may be entered employing a username and countersign. it's accessible either by associate administrator or secretarial assistant. entirely they will add awareness into the information may be recovered simply. The proposed system is especially not difficult. the information are well securedforpersonal useandmakesthedetailsofprocess innotime.

1. INTRODUCTION

Now a day’s Critical Patient Caring or monitoring System is a process where a doctor can continuously monitor more than one patient, for more than one parameter at a time in a distant place and also can have command over medicine dosage Development and evaluation of the ICU decision-support systems would be greatly facilitated by these systems. Virtual Expert prediction is a web Application which provide complete health care system providing the end user with a responsive User Interface, whereintheusercanenterallthevitalsignsrelatedtothe patient using many predefined options. Moreover, this application is designed for the particular need of the user tocarryouthealthexaminationsinasmoothandeffective manner. This application can be used to reduce human error as much as possible in the field of medical science. it'snotnecessarythatuserisneededformalknowledgeto use this system. Thus, by this all advantages proposed systemprovesitisuser-friendly.VirtualExpertprediction web application is, as described above, it can predict primary stage of disease that can lead to safe, and secure, reliable and precise systems it will help particular person fromdeath.

1.1 AIM

The aim is to provide as it is not intended for a particular organization.Thisprojectisgoingtodevelopagenericweb application, which can be applied by any health organization or government in future. Moreover, it provides facilities to its citizens. Also, the web application isgoingtoprovideahugeamountofsummarydata.

1.2 OBJECTIVE

The main objective of this project is basically targeted to provide health related services to remote places and provide better health care and improve national health. Citizens can be instantly examined using this system whichwill beavailabletodoctors,sevika,NGO,screeners, also it will provide proper diagnosis, maintain medical recordsandwillbeeasilyavailabletoall.

2. LITERATURE SURVEY

A. Kidney Diseases

Serek et al. [12] planned a comparative study of classifiers performance for Chronic Kidney disease (CKD) detection using The Kidney Function Test (KFT) dataset. In this study, the classifiers used are KNN, NB, and RF classifier; their performance is examined in terms of F-measure, precision, and accuracy.Asperanalysis,RFscoredbetterinphrases of F-measure and accuracy, while NB yielded better precision. In consideration of this study, Vijayarani [13] aimed to detect kidney diseases using SVM and NB.Theclassifierswereusedtoidentifyfourtypesof kidney diseases namely Acute Nephritic Syndrome, AcuteRenalFailure,ChronicGlomerulonephritis,and CKD.

B. Heart Diseases

Marimuthuetal.[16]aimedtopredictheartdiseases using supervised ML techniques. The authors structuredtheattributesofdataasgender,age,chest pain, gender, target and slope [16]. The applied ML algorithms that were deployed are DT, KNN, LR and

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056

Volume: 09 Issue: 12 | Dec 2022 www.irjet.net p-ISSN: 2395-0072

NB. As per analysis, the LR algorithm gave a high accuracy of 86.89%, which deemed to be the most effective compared to the other mentioned algorithms. In 2018, Dwivedi [17] attempted to add moreprecisiontothepredictionofheartdiseasesby accountingforadditionalparameterssuchasResting blood pressure, Serum Cholesterol in mg/dl, and MaximumHeartRateachieved.Theuseddatasetwas imported from the UCI ML laboratory; it was comprisedwith120samplesthatwereheartdisease positive, and 150 samples that were heart disease negative. Dwivedi attempted to evaluate the performance of Artificial Neural Networks (ANN), SVM,KNN,NB,LRandClassificationTree.

C. Breast Diseases

Shubair [20] attempted for the detection of breast cancer using ML algorithms, namely RF, Bayesian Networks and SVM. The researchers obtained the Wisconsin original breast cancer dataset from the UCI Repository and utilized it for comparing the learning models in terms of key parameters such as accuracy, recall, precision, and area of ROC graph. The classifiers were tested using K-fold validation method, where the chosen value of K is equal to 10 [20].

D. Parkinson’s Disease

Chen et al. [22] presented an effective diagnosis system using Fuzzy k-Nearest Neighbor (FKNN) for thediagnosisofParkinson’sdisease(PD).Thestudy focused on comparing the proposed SVM-based and the FKNN-based approaches. the Principal Component Analysis (PCA) was utilized to assemble the most discriminated features for the construction of an optimal FKNN model. The dataset was taken from the UCI depository, and it recorded numerous biomedical voice measurement ranging from 31 people, 24 with PD. The experimental findings have indicated that the FKNN approach advantageously achieves over the SVM methodology in terms of sensitivity,accuracy,andspecificity.

E. A. Common Diseases

Dahiwadeetal.[9]proposedaMLbasedsystemthat predicts common diseases. The symptoms dataset was imported from the UCI ML depository, where it contained symptoms of many common diseases. The system used CNN and KNN as classification techniques to achieve multiple diseases prediction. Moreover, the proposed solution was supplemented with more information that concerned the living habits of the tested patient, which proved to be helpful inunderstanding thelevel ofrisk attachedto the predicted disease. Dahiwade et al. [9] compared

theresultsbetweenKNNandCNNalgorithminterms of processing time and accuracy. The accuracy and processing time of CNN were 84.5% and 11.1 seconds,respectively.ThestatisticsprovedthatKNN algorithm is under performing compared to CNN algorithm. In light of this study, the findings of Chen etal.[10]alsoagreedthatCNNoutperformedtypical supervisedalgorithmssuchasKNN,NB,andDT.The authors concluded that the proposed model scored higherintermsofaccuracy,whichisexplainedbythe capability of the model to detect complex nonlinear relationships in the feature space. Moreover, CNN detects features with high importance that renders better description of the disease, which enables it to accuratelypredict diseases withhighcomplexity[9], [10]. This conclusion is well supported and backed with empirical observations and statistical arguments. Nonetheless, the presented models lacked details, for instance, Neural Networks parameters such as network size, architecture type, learningrateandbackpropagationalgorithm,etc.In addition, the analysis of the performances is only evaluated in terms of accuracy, which debunks the validity of the presented findings [9]. Moreover, the authors did not take into consideration the bias problem that is faced by the tested algorithms [9], [10]. In illustration, the incorporation of more feature variables could immensely ameliorate the performance metrics of under performed algorithms [11].

-1:LiteratureSurvey

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056

Volume: 09 Issue: 12 | Dec 2022 www.irjet.net p-ISSN: 2395-0072

– Questionnaire

Here the user feed the values in the application form, he/shefillsupeachandeverydetailsintheform.Allthese details gets saved in the server and details and from that we can extract the features of the disease. The entered details are matched with the datasets which are saved in thedatabase.

– Check for the disease symptoms:

After matching the details with the datasets it checks for the disease symptoms. One feature may match with different disease. So, it’s necessary to check each and every matched details in order to predict the correct disease.

– Generate Report:

Fig -1:SystemArchitecture

Ourproposedsystemallowshealthprofessionalstoreach anycornerofthenationby havingaremoteclinicattheir fingertips. Instant examination can be carried out from anywhere where a person registered first, his vital sign willbemeasured,symptomswillbegatheredandareport will be generated based on the above inputs. Thus every registeredmemberwillbelinkedbyauniquenumberthat willbetheAADHARUIDwhichinmajoritywillreflectthe national health scenario. Past medical records as well as the treatment procedure can be stored which will be further useful to provide better treatment based on past records. Computer generated prescription will further eliminate false prescription and irregularities in the pharmaceuticals.

3. METHODOLOGY

Methodology is a process that mainly consists of intellectual activities usually only the end goal of the methodology process is manifested as the product or result of the physical work. In software, the term methodology is used to refer to series of steps or a procedure which governs the activities of analysis and guidelines to design or an organized documented set of procedures and guidelines for one or more phases of the (software life cycle), such as analysis or design. Any project is basically divided into many groups for easy understandingandcoding.

- User Registration:

Logging in, (or logging on or signing in or signing on), is the process by which an individual gains access to a computer system by identifying and authenticating themselves. The user credentials are typically some form of “username” and a matching “password”, and these credentials themselves are sometimes referred to as a login.

Areportwillgenerateonthebasisofsymptomswhichare matched.Itpredictsthediseaseandsendittousermobile application, and finally add some tips/suggestions to the user like nearby hospital details and it notifies patient by sendingamessagealerttopatientmobilenumber.

3.1 ALGORITHMS

3.1.1

Naïve Bayes Algorithm:

Naivebayes algorithmismainlyusedintextclassification that includes a high-dimensional training dataset one of the simple and most effective Classification algorithms which helps in building the fast machine learning models thatcanmakequickpredictions.

3.1.2 Support Vector Machine (SVM):

ThemainaimofSVMalgorithmistocreatethebestlineor decisionboundarythatcansegregaten-dimensionalspace intoclassessothatwecaneasilyputthenewdatapointin the correct category in the future. The best decision boundary in SVM is called a hyper plane. This algorithm chooses the extreme points or vectors that will help in creatingthehyperplane.

4. CONCLUSIONS

The conclusion is that our system will assist medical actors in their processes to enhance diagnostics Capabilities, treatment procedures, prescriptions and recommendations; and a creation of cooperative techniques for agents in a distributed medical environment.Anothermajorcontributionofthisapproach is the cost effectiveness of its implementation in terms of using and adapting the existing healthcare services together with information sources. In order to benefit fromutilizingtheproposedsystem,medicalactorsrequire modification of their business processes and changing

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056

Volume: 09 Issue: 12 | Dec 2022 www.irjet.net p-ISSN: 2395-0072

their behavior in using new technologies. This approach willimprovetheproductivityofthemedicalprofessionals andthequalityofhealthcareinGeneral.

REFERENCES

[1] Amin, S.U.; Agarwal, K.; Beg, R., "Genetic neural network based data mining in prediction of heart disease using risk factors", IEEE Conference on Information & Communication Technologies (ICT), 2013, vol., no.,pp.1227-31,11-12April2013.

[2] Palaniappan S, Awang R, "Intelligent heart disease prediction System using data mining techniques," IEEE/ACS International Conference on Computer Systems and Applications, AICCSA 2008., vol., no., pp.108- 115, March312008-April42008.

[3] Cirkovic, Bojana R.Andjelkovic; Cvetkovic, Aleksandar M, Ninkovic, Srdjan M, Filipovic, Nenad D., "Prediction models for estimation of survival rate and relapse for breast cancer patients", IEEE 15th International Conference on Bioinformatics and Bioengineering (BIBE), vol.,no.,pp.1-6,2-4Nov.2015.

[4] Telecom Italia Lab, Java Agent Development Framework,http://jade.tilab.com/.

[5] Agent Oriented Software Limited (AOS), JACK Documentation, http://www.aosgrp.com/products/jack/documentation andinstruction/jackdocumentation.html.

[6] Khajehpour, Salari & E. Raheleh, (2013) “Advancements and Trends in Medical Case-Based Reasoning: An Overview of Systems and System Development”,IranJ.MedicalInformatics,Vol.2,No.4,pp 12-16.

[7] P. B. Chanda and S. K. Sarkar, "Cardiac MR Images SegmentationFor Identification OfCardiac DiseasesUsing FuzzyBasedApproach,"IEEE,ICSSIT2020,pp.1238-1246, Aug2020

[8] Kavakiotis I, Tsave O, Salifoglou A, Maglaveras N, VlahavasI,ChouvardaI.Machinelearninganddatamining methodsindiabetesresearch.ComputStructBiotechnolJ. 2017;15:104–16

[9] Kourou K, Exarchos TP, Exarchos KP, Karamouzis MV, Fotiadis DI. Machine learning applications in cancer prognosis and prediction. Comput Struct Biotechnol J. 2015;13:8–17.

[10] 36. Alizadehsani R, Roshanzamir M, Abdar M, BeykikhoshkA,KhosraviA,PanahiazarM,etal.Adatabase forusingmachinelearninganddataminingtechniquesfor coronaryarterydiseasediagnosis.SciData.2019;6:227.

AUTHORS DETAILS

SNEHALJ BHOIR, Dept.ofComputerEngineering, Shatabdi Institute of Engineering andResearch,Maharashtra,India

PRITIS.DESHMUKH Dept.ofComputerEngineering, Shatabdi Institute of Engineering andResearch,Maharashtra,India.

PRAJAKTAD.JADHAV Dept.ofComputerEngineering, Shatabdi Institute of Engineering andResearch,Maharashtra,India

MOHINIP WAGH Dept.ofComputerEngineering, Shatabdi Institute of Engineering andResearch,Maharashtra,India.

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