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Depression Analysis Using Supervised Models

Authors: G. Ganapathy Babu;

Depression Analysis Using Supervised Models

Abstract

Depression is one of the severe and gravehealthdisordersthataffectsthesteadinessofmind.Ithasbecomeaseriousissueinthepresentgeneration.Thetotalnumber of cases has been increasing day-by-day due to anumber of reasons like stress at school, college, work,personal life, other diseases, etc. Although it has becomeoneofthemostcommondisease,peoplearestillreluctantto talk about it openly due to the fear that others mightconsiderthemlunatic.TheintroductionofMachineLearning into the field of Medicine and Health industryhasprovideddiagnostictoolsthatareabletoenhancetheprecision and accuracy while reducing the difficult taskswhichrequiretheinterventionofhumans.ThereispromisingevidencethatMachineLearninghasthecapabilitynotonlytodetectbutalsosignificantlyenhancethe treatment of compound mental conditions such asdepressionbydevelopingaframework.Inthepast,Machine Learning Algorithms have been proved to befairly supportive where researchers worked on the datafromsocialmediatoforeseethenumberofpersonssuffering from this ailment on the basis of their initialsymptoms. The main aim is to help those patients whosufferfromdepressionintheearlyrecognitionofsymptoms of this disease which can prove to be valuablenotonlytothembutalsototheirfamilies.

Keywords

Depression, Machine Learning, Naïve BayesAlgorithm,SupportVectorMachine,DecisionTree,Random Forest, KNearest NeighbourClassifier.

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This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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