Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Suicidal behavior prediction using data mining techniques Subject The topic of the resource Classification; Data Mining; Depression; Prediction; Risk Factors; Suicide Description An account of the resource Background: Suicide is one of the most serious public health problem that has affected many people. After being recognized as a public health priority by the WHO (World Health Organization) various studies have been going out for its prevention. It is one of a serious health problem and it is preventable and can be controlled by proper interventions and study in the field. The objective of the study is to create a prediction model for individuals who are at higher risk of suicide by studying the different predictors of suicide such as depression, anxiety, hopelessness, stress etc. by using data mining techniques for the prediction. Study Design: Systematic review and predictive analysis for suicidal behavior. Methods: The research applies data mining process to analyze the data and on the basis of analysis create the model to predict suicidal behaviors present in the individual. Prediction is done on the basis of analysis of risk factors which are Depression, anxiety, hopelessness, stress, or substance misuse which is calculated by using various psychological measures such as Beck hopelessness scale,suicidal ideation subscale,hospital anxiety and depression scale.Various data mining algorithms for classification are compared for the purpose of prediction. Results: Six different data mining classification algorithms which are namely Classification Via Regression, Logistic Regression. Random Forest, Decision Table, SMO are compared and Classification Via Regression was found to the highest accuracy in prediction. Conclusions: Data required for the development of such a model requires continuous monitoring and needs to be updated on a periodic basis to increase the accuracy of prediction. IAEME Publication. Creator An entity primarily responsible for making the resource Joseph A.; Ramamurthy B. Source A related resource from which the described resource is derived International Journal of Mechanical Engineering and Technology, Vol-9, No. 4, pp. 293-301. Publisher An entity responsible for making the resource available IAEME Publication Date A point or period of time associated with an event in the lifecycle of the resource 2018-01-01 Identifier An unambiguous reference to the resource within a given context <a href="" target="_blank" rel="noreferrer noopener"></a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85045951449&amp;partnerID=40&amp;md5=74f2c78c8b63037785fb7ed22ef9b8a1" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85045951449&amp;partnerID=40&amp;md5=74f2c78c8b63037785fb7ed22ef9b8a1</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 9766340 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Joseph A., Department of computer science, Christ University, Bangalore, Karnataka, India; Ramamurthy B., Department of computer science, Christ University, Bangalore, Karnataka, India