International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 11 Issue: 04 | Apr 2024 www.irjet.net p-ISSN: 2395-0072
Stress Prediction in Working Employees Using Machine Learning
Dr. H K Chethan1, Ms. Chaithrashree S2, Mr. Likith Rao A3, Ms. Pavithra S4, Ms. Syeda Aalia Zareen5
1 Professor, Dept. of Computer Science and Engineering, Maharaja Institute of Technology, Thandavapura 2,3,4,5Students, Dept of Computer Science and Engineering, Maharaja Institute of Technology, Thandavapura ***
Abstract - Intoday'sITlandscape,employeestressisavery significantconcern,impactingmentalhealthandworkplace productivity.Inthisproject,areal-timeapplicationthatuses profiles to forecast stress levels in working professionals is introduced. Stress is difficult for the present manual procedures to identify, hence an automated solution is required. Our suggested approach uses the norms of data science classification to divide employees into two groups: StressedandStress-Free.Byproactivelymanagingemployee stress, the main objective is to improve decision-making proceduresand,eventually,corporatethefinalresultsrelated to stress. VisualStudioandSQLServerwereusedtocreatethesystem,w hichis a browser-based program that can be accessed bymanyusersandlocations.Thisprojectsupportsthelarger goal of emphasizing employee well-being inside the organization in addition to addressing the urgent need for stressprediction.
Key Words: Real-time application, Profiles, Forecasting, Stress levels, IT professionals, Automated solution, Data science, Classification, Norms, Proactive management.
1.INTRODUCTION
In the ever-evolving landscape of the Information Technology (IT) industry, the wellbeing of employees is a crucial aspect. IT professionals often face mental health issues namely stress, depression, and interpersonal sensitivity. Despite efforts by industries to address these issues, manual identification and intervention remain the norm.Ourprojectseekstorevolutionize thisapproachby introducing an automated Stress Prediction System for IT employees.
Thecurrentmanualsystemstrugglestopromptlyidentify stress among employees, leading to a lack of timely intervention. Oursolutionaimstofill thisgapwitha realtimeapplicationusingdatasciencetechniquestopredictthe stresslevelsbasedonworkingemployeeprofiles. Byleveraging"classificationrules,"weaimtoprovideauserfriendly tool for categorizing employees into stress and stress-freegroups.
This project's scope extends to the business sector, specificallytargetingstresspredictionwithintheITindustry. Theproposedsystemisanautomationsolutionaccessiblein
real-time through a browser-based application. By addressingthepressingissueofemployeestress,ourproject strives to create a positive impact on the well-being and productivityofITprofessionals,fosteringahealthierwork environment.
1.1 OBJECTIVE
Thesystemisareal-timeprogramdesignedtoforecastan employee'sstresslevelwhiletheyareatwork.Theworking employee is categorized by the model as either Stress or Stress free. Better decision-making and business improvisationareincludedinthescope.
1.2 MOTIVATION TO TAKE UP THE PROBLEM
The inspiration for resolving the issue of articulation of stress expectations among working representatives in IT organizations lies in the critical effect that pressure has. Workerstressisatypicalissueinthehigh-speedITbusiness oftoday,yetitismuchofthetimeneglectedinlightofthe fact that manual methods are utilized to recognize it. The criticalnessandsignificanceofthisexaminationarefeatured by the current condition of pressure expectation, which needs robotization. We get the opportunity to foster a framework that proactively recognizes and oversees pressureinITexperts,advancingamorecertainanduseful workplace,byusingtheforceofinnovationandinformation science.
1.3 CHALLENGES TO BE ADDRESSED
The trouble of really perceiving pressure pointers and evaluatingdifferentworkerprofilesmakesitchallengingto expectrepresentativepressureinITassociationsutilizingAI calculations. Right now, the absence of a computerized frameworkworsenstheissue,contingentjustuponmanual techniquesthateverynowandagainfinditchallengingto recognize little signs of pressure. Conquering hindrances such as information assortment and quality, including choice,modelpreparation,andinterpretability,isimportant to foster areas of strength for a Moreover, the framework shouldbeadaptableandfunctionalforuseingenuinework settings.Topropelworkerprosperityendeavorsandamplify hierarchicalexecution,theseissuesshouldbesettled.
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 11 Issue: 04 | Apr 2024 www.irjet.net p-ISSN: 2395-0072
1.4 NAIVE BAYES CLASSIFIER
Forordererrands,apopularanddirectprobabilisticmodel istheGullibleBayesclassifiercalculation.The"credulous" suppositionofcomponentfreedomshapesthegroundwork of Bayes' hypothesis. Despite its effortlessness, Gullible Bayesoftenworkssuccessfullyingenuinecircumstancesand isparticularlyusefulincircumstanceswithalotofelements. Itdecidestheprobabilitythatagivenexamplewillfallintoa particularclassbycomputingtheprobabilitythatelements will happen in that class. In view of its basic execution, extraordinaryfiguringproductivity,andabilitytodealwith high-layeredinformation,GullibleBayesisgenerallyusedin numerousapplications,forexample,spamseparating,text characterization,andclinicaldetermination.
The Naive Bayes classifier algorithm presents a viable method for anticipating stress levels among IT industry workers. Based on Bayes' theorem and predicated on the "naive"assumptionoffeatureindependence,NaiveBayesis astraightforwardbutpowerfulprobabilisticclassifier.This algorithm can effectively handle high-dimensional data, which is very useful in situations where there are a lot of attributes,asisoftenthecasewithemployeeprofiles.Naive Bayes can accurately categorize people into stressed or stress-free groups by using past data on employee characteristicsandstresslevels.Itworkswellinreal-world applicationswheredatamaybenoisyorpartialbecauseof itscapacitytomanagesuchdata.Moreover,thealgorithm's computationalefficiencymakesiteasiertointegrateitinto automatedstresspredictionsystems,addressingstress.
1.5 LITERATURE SURVEY
An Numerous studies have tackled the problem of stress predictionusingdifferentmethods,suchasdataminingand machine learning algorithms, according to the literature reviewthatwassupplied.Belowisasynopsisofthebenefits anddrawbacksmentionedinthesepapers:
1.Thestudy"ClassificationAlgorithmsbasedMentalHealth Prediction using Data Mining" by Vidit Laijawala et al. (2022): Employs data mining methods to predict mental health, relies on small datasets, leading to less accurate results. Massive amounts of data are required for implementing the project.
2.U Srinivasulu Reddy et al. (2020) "Machine Learning Techniques for Stress Prediction in Working Employees" examines stress patterns in people who work by using machinelearningtechniques.Lessparametersareusedto forecaststress.Real-timeapplicationsmightnotbeagoodfit fortheboostingalgorithm.
3."Predictive Analysis of Student Stress Level using Naive Bayesian Classification Algorithm" by Monisha S. et al. (2020): precise levels of stress among students. One
potential problem could be the algorithms' lengthy processingtimes.lowefficacyoftheresults.Thisisjustfor usagebycollegestudents.
4.Fang Li (2016) "Research on the College Student's PsychologicalHealthManagementbasedonDataMiningand CloudPlatform"examineshowcollegestudentstakecareof theirpsychologicalwell-beingbyutilizingcloudcomputing and data mining technologies. Restricted parameters and high efficiency in outcomes; mostly applicable to the educationalfield.
All things considered, these studies provide insightful analysesofstressprediction;yet,therearemanydrawbacks, includingissueswithdatavolume,algorithmperformance, demographic applicability, and the requirement for more thorough criteria for stress prediction that works in the workplace.
2. EXISTING SYSTEM
IT laborers in the ongoing framework manage emotional wellness issues such as uneasiness, pressure, misery, relationalresponsiveness,dread,andapprehension,among others. Despite the fact that a ton of organizations and venturesofferpsychologicalwellnessrelatedprojectsand trytofurtherdeveloptheworkplace,theissueiscrazy.The subtletiesoftheongoingframeworkmakeitclearthatitisa mind-bogglingframeworkrequiringalotofhumanwork.It requiresagreatdealofinvestmentandrequiresmasteryand experience, and manual counsel might be less exact and proficient.
3. PROPOSED SYSTEM
Frameworks distinguish factors that fundamentally influence feelings of anxiety, such as orientation, family ancestry, and the accessibility of medical advantages at work,whichareviewedascriticalelementsinpressure.The framework pulls information from different sources, including orientation, age, family ancestry, electronically provided medical advantages, illness revelation, tech organization, tech capability, and leave securing. The frameworkidentifiesarepresentative'spressurebyutilizing man-made reasoning (computer based intelligence) or AI procedures.ITassociationscanprofitfromtheimprovement ofaframeworkthatcanworkasaconstantapplication.The primary objective of the framework is to distinguish the gamblingfactorsthataffecttheemotionalwell-beingofthe workers.
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 11 Issue: 04 | Apr 2024 www.irjet.net p-ISSN: 2395-0072
SYSTEM ARCHITECTURE
Fig 3: System Architecture
Therearenumerouscrucialstepsinthesystemdesignfor stress prediction in IT workers. First, a series of queries pertainingtotheprofilesandstresslevelsofthepersonnel serve as the input data. Comprehending the features and trends included in the datasets is a prerequisite for data understanding.Thenextstepinvolvespreparingthedatato makeitcleaner,morestandardized,andreadyforanalysis. Themodelissubsequentlytrainedusingthepre-processed datausingsupervisedlearningalgorithms.Itisnecessaryto evaluatethealgorithminordertodetermineitscorrectness and performance. Employers are categorized into stress levels according to their profiles in the stress prediction process, which makes use of the solution from the supervisedlearningstage.Theefficacyofthesystemisthen assessedbasedontheoutcomes,whichincludeindicators
foraccuracyandefficiency.Andlastly,informationisstored andrepresented.
5. DATASETS
1.A dataset is a collection of data, often organized in a structuredorsemi-structuredformat,thatisutilised fora specific purpose, such as research, analysis, or machine learning.
2.Once a dataset is captured, it often undergoes preprocessing steps to clean and format the data appropriately for the intended use, such as training a machinelearningmodel.
3.Datasetisthecollectionofattributestothemodel.Wehave downloadedthedatasetsfromGoogleinthe.csvfileformat. Input and Output: Input - Systemusesmanyparameters such as gender, age, family history, e-provided health benefits, share about illness, tech company, tech role, acquiringleaveetc.andolddata-setsforprocessing. Output – classifiestheemployeesintoStressandStressFree.
6. IMPLEMENTATION AND RESULT
Therearedifferentadvancesassociatedwithapplyingthe Bayes classifier to stretch forecasts for working representatives. To start with, we accumulate data on a scope of worker qualities, including responsibility, work fulfillment, relational associations, and actual wellbeing markers.Fromthatpointonward,wetidyupanddesignthe informationtoprepareitforexaminationasafeatureofthe pre-processingstep.TheNaiveBayesclassifiercalculationis thenutilized,whichutilizesthepre-handledinformationto learn and foresee a representative's probability of encounteringpressureinlightoftheirprofile.Utilizingthis strategy, we had the option to foresee representative feelingsofanxietyinourreviewatanexactnessofmorethan 90% and higher. In light of their qualities, the Credulous Bayes classifier can really recognize pushy and calm representatives,asprovenbyitshighprecision.Theresults willthenbeshown.
7. CONCLUSIONS
Taking everything into account, applying AI strategies to estimate individuals' pressure and emotional well-being conditions shows empowering results and fits with the objectives of this review. The proposed approach handles the pivotal issue of pressure forecasts as well as further developingnavigationandbusinessresultsusingorderrules inaconstantapplication.Theinvestigationofstress-related issues has not forever been a major area of strength for conventional information mining techniques; in any case, thisissuecanbetendedtowiththeutilizationofAIdrawing near.Bycoordinatingmodifiedproposalsinlightofanxiety
Fig 1: Sequence Diagram (Admin)
Fig 2: Sequence Diagram (Employee)
4.
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 11 Issue: 04 | Apr 2024 www.irjet.net p-ISSN: 2395-0072
estimates,theinnovationempowersindividuals,particularly IT laborers, to deal with their psychological wellness effectively. At last, the utilization of AI strategies to push expectationsproducesvitalresults.
ACKNOWLEDGEMENT
We wish to express our deepest appreciation to our esteemedProjectGuide,Dr.HKChethan,whoseinvaluable guidanceandsuggestionshavepropelledourprojectbeyond ourexpectations.Weextendourheartfeltgratitudetoour Project Coordinator, Dr. H K Chethan, for his unwavering supportanddedicationinhelpinguscompletethisproject within a tight time-frame. We would also like to acknowledge our Head of Department, Dr. Ranjit KN, for fostering an environment that encourages innovation and practicalapplicationofouracademiccurriculum.Finally,we extendoursincerestthankstoourPrincipal,Dr.YTKrishne Gowda,forprovidinguswithagoldenopportunitytocarry out project on the topic of ‘Stress Prediction in Working EmployeesusingMachineLearning',andforhisunwavering supportinourresearchandlearningendeavors.
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