Astraphobia and Cynophobia Dataset

Citation Author(s):
Alina
Munir
University of Engineering and Technology, Lahore, Pakistan.
Submitted by:
Alina Munir
Last updated:
Wed, 02/09/2022 - 10:36
DOI:
10.21227/vy8c-nb39
License:
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Abstract 

Today, Mental health problems are getting grave and need technological solutions. Irrational anticipated fear is Anxiety Disorder. Specific Phobia disorders are a type of Anxiety disorder; these phobias are rarely detected in clinical settings and are presence indicators of other serious mental problems. VR is considered a potent tool for treatment and diagnosis. In this study, we investigated the parameters for predicting participants’ severity level of Cynophobia and Astraphobia by using the following measures: “APA Specific Phobia Severity Measure - Adult”, “ Distance and Time”, “Heart Rate and Oxygen levels for each level” in VR-specific-phobia diagnostic environment, “symptoms” observed during experimentation, and “causes” described by DSM-5. The “APA Specific Phobia Severity Measure - Adult” is attributed as the standard used by psychiatrists for clinical evaluation. We used the score of this measure to classify instances for each participant. The other parameters serve as attributes for predicting class, implementing the process of Data Mining. The literature supports all the prior mentioned parameters for assessing severity levels for specific phobia. The participant walks or runs along a road in a Virtual Reality Environment to achieve the objective. The first scenario is a neutral environment with no phobic stimulus; the afterward situations pose for a dog cue, thunder lightning stimulus, and a combination of both stimulation consecutively. The ‘Distance’ traveled and ‘Time’ taken in units for each VR scenario generated using a Bluetooth controller is saved in a file with time stamps. The participant subsequently fills Google Form to record the parameters. The dataset is converted to ARFF format, and the process of Knowledge Discovery is applied using the WEKA tool. The results suggest that the presence of Cynophobia and Astraphobia are highly interrelated. The study advised that Dog-Phobia severity level confidently predicts with the parameters “Age”, “Time” in Neutral scenario, “Distance” covered in Cynophobic scenario”, “ Difference in Oxygen levels” of Cynophobic VRE and scenario with both (Dog and Thunder Lightning) stimuli and “DSMAstraphobia”. The research analysis concludes that thunder-lightning phobia severity level effectively forecasts with these attributes: “Velocity”, “Distance” and “Time” in Neutral VRE scenario”; “Velocity”, “Time” VRE scenario for both pre-mentioned phobic stimuli; “Time” in Cynophobic scenario, “Velocity” calculated in Astraphobic VRE, “Age” of the participant and DSMCynophobia. This study will help in suggesting standards for diagnosing mental health problems with the advantages of VR.

Instructions: 

The Data.Zip consists of 5 files. These files are

  1. CynopAstraPhobia_100.arff
  2. Cyno_Preprocessed.arff
  3. Astra_Preprocessed.arff
  4. comparison tables.xlsx
  5. Steps data cleaning.txt

 

The first file consists of 100 instances recorded with measurements for assessment of Cynophobia and Astraphobia. The 2nd and 3rd files are preprocessed for applying Machine Learning algorithms to discover knowledge. The 4th file is the comparison of performance metrics of ML algorithms for the various subset of selected parameters. The 5th file is the list of steps taken for data preprocessing. 

 

WEKA software is used for data mining.