Identifying the Symptom Severity in Obsessive-Compulsive Disorder for Classification and Prediction: An Artificial Neural Network Approach
The present study is aimed at identifying the most prominent determinants of OCD along with their strength to classify the OCD patients from healthy controls. The data for this cross-sectional study were collected from 200 diagnosed OCD patients and 400 healthy controls. The respondents were selecte...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-01-01
|
| Series: | Behavioural Neurology |
| Online Access: | http://dx.doi.org/10.1155/2020/2678718 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850226113578532864 |
|---|---|
| author | Mirza Naveed Shahzad Muhammad Suleman Mirza Ashfaq Ahmed Amna Riaz Khadija Fatima |
| author_facet | Mirza Naveed Shahzad Muhammad Suleman Mirza Ashfaq Ahmed Amna Riaz Khadija Fatima |
| author_sort | Mirza Naveed Shahzad |
| collection | DOAJ |
| description | The present study is aimed at identifying the most prominent determinants of OCD along with their strength to classify the OCD patients from healthy controls. The data for this cross-sectional study were collected from 200 diagnosed OCD patients and 400 healthy controls. The respondents were selected through purposive sampling and interviewed by using the Y-BOCS scale with the addition of a factor, worth of an individual in his family. The validity and reliability of data were assessed through Cronbach’s alpha and confirmatory factor analysis. Artificial Neural Network (ANN) modeling was adopted to determine threatening determinants along with their strength to predict OCD in an individual. The results of ANN modeling depicted 98% accurate classification of OCD patients from healthy controls. The most contributing factors in determining the OCD patients according to normalized importance were the contamination and cleaning (100%); symmetric and perfection (72.5%); worth of an individual in the family (71.1%); aggressive, religious, and sexual obsession (50.5%); high-risk assessment (46.0%); and somatic obsessions and checking (24.0%). |
| format | Article |
| id | doaj-art-52b4d0ff87dc483782aeb5a45f0bc06a |
| institution | OA Journals |
| issn | 0953-4180 1875-8584 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Behavioural Neurology |
| spelling | doaj-art-52b4d0ff87dc483782aeb5a45f0bc06a2025-08-20T02:05:10ZengWileyBehavioural Neurology0953-41801875-85842020-01-01202010.1155/2020/26787182678718Identifying the Symptom Severity in Obsessive-Compulsive Disorder for Classification and Prediction: An Artificial Neural Network ApproachMirza Naveed Shahzad0Muhammad Suleman1Mirza Ashfaq Ahmed2Amna Riaz3Khadija Fatima4Department of Statistics, University of Gujrat, PakistanDepartment of Statistics, University of Gujrat, PakistanDepartment of Management Sciences, University of Gujrat, PakistanDepartment of Statistics, University of Gujrat, PakistanDepartment of Statistics, University of Gujrat, PakistanThe present study is aimed at identifying the most prominent determinants of OCD along with their strength to classify the OCD patients from healthy controls. The data for this cross-sectional study were collected from 200 diagnosed OCD patients and 400 healthy controls. The respondents were selected through purposive sampling and interviewed by using the Y-BOCS scale with the addition of a factor, worth of an individual in his family. The validity and reliability of data were assessed through Cronbach’s alpha and confirmatory factor analysis. Artificial Neural Network (ANN) modeling was adopted to determine threatening determinants along with their strength to predict OCD in an individual. The results of ANN modeling depicted 98% accurate classification of OCD patients from healthy controls. The most contributing factors in determining the OCD patients according to normalized importance were the contamination and cleaning (100%); symmetric and perfection (72.5%); worth of an individual in the family (71.1%); aggressive, religious, and sexual obsession (50.5%); high-risk assessment (46.0%); and somatic obsessions and checking (24.0%).http://dx.doi.org/10.1155/2020/2678718 |
| spellingShingle | Mirza Naveed Shahzad Muhammad Suleman Mirza Ashfaq Ahmed Amna Riaz Khadija Fatima Identifying the Symptom Severity in Obsessive-Compulsive Disorder for Classification and Prediction: An Artificial Neural Network Approach Behavioural Neurology |
| title | Identifying the Symptom Severity in Obsessive-Compulsive Disorder for Classification and Prediction: An Artificial Neural Network Approach |
| title_full | Identifying the Symptom Severity in Obsessive-Compulsive Disorder for Classification and Prediction: An Artificial Neural Network Approach |
| title_fullStr | Identifying the Symptom Severity in Obsessive-Compulsive Disorder for Classification and Prediction: An Artificial Neural Network Approach |
| title_full_unstemmed | Identifying the Symptom Severity in Obsessive-Compulsive Disorder for Classification and Prediction: An Artificial Neural Network Approach |
| title_short | Identifying the Symptom Severity in Obsessive-Compulsive Disorder for Classification and Prediction: An Artificial Neural Network Approach |
| title_sort | identifying the symptom severity in obsessive compulsive disorder for classification and prediction an artificial neural network approach |
| url | http://dx.doi.org/10.1155/2020/2678718 |
| work_keys_str_mv | AT mirzanaveedshahzad identifyingthesymptomseverityinobsessivecompulsivedisorderforclassificationandpredictionanartificialneuralnetworkapproach AT muhammadsuleman identifyingthesymptomseverityinobsessivecompulsivedisorderforclassificationandpredictionanartificialneuralnetworkapproach AT mirzaashfaqahmed identifyingthesymptomseverityinobsessivecompulsivedisorderforclassificationandpredictionanartificialneuralnetworkapproach AT amnariaz identifyingthesymptomseverityinobsessivecompulsivedisorderforclassificationandpredictionanartificialneuralnetworkapproach AT khadijafatima identifyingthesymptomseverityinobsessivecompulsivedisorderforclassificationandpredictionanartificialneuralnetworkapproach |