Depression Analysis Using Supervised Models

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.