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Related Concept Videos

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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
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Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
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A comparative study of machine learning techniques for suicide attempts predictive model.

Noratikah Nordin, Zurinahni Zainol, Mohd Halim Mohd Noor1

  • 1Universiti Sains Malaysia, Malaysia.

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|March 22, 2021
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Summary
This summary is machine-generated.

Machine learning models can predict suicide attempts in depressed patients with 92% accuracy. Ensemble models, particularly voting and bagging, show superior performance over single models for suicide risk assessment.

Keywords:
data miningdepressive disordermachine learningpredictive modelsuicidal behaviour

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Area of Science:

  • Psychiatry
  • Computer Science
  • Data Science

Background:

  • Current suicide risk assessments lack predictive value and reliability.
  • There is a need for improved methods to identify suicide attempts in patients with depression.

Purpose of the Study:

  • To develop and compare machine learning models for predicting suicide attempts in depressed patients.
  • To differentiate between depressed patients with and without a history of suicide attempts.

Main Methods:

  • Applied and trained eight machine learning algorithms on a dataset of 75 patients with depressive disorder.
  • Utilized recursive feature elimination and three-fold cross-validation to reduce features.
  • Compared the performance of single predictive models against ensemble predictive models.

Main Results:

  • Ensemble predictive models significantly outperformed single predictive models.
  • Voting and bagging ensemble models achieved the highest accuracy of 92%.
  • Key predictors identified include history of suicide attempt, religion, race, suicide ideation, and depression severity.

Conclusions:

  • Machine learning, especially ensemble methods, offers a promising approach for accurate suicide attempt prediction in depressed individuals.
  • History of suicide attempt, demographic factors, ideation, and depression severity are crucial for predictive modeling.