Indian J Sex Transm Dis Indian J Sex Transm Dis
Official Publication of the Indian Association for the Study of Sexually Transmitted Diseases
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ORIGINAL ARTICLE
Year : 2018  |  Volume : 39  |  Issue : 1  |  Page : 50-54

Deploying machine learning to find out the reasons for not using condom in a questionnaire-based study of 120 patients


1 Department of Dermato-Venereo-Leprology, Government Thiruvarur Medical College, Thiruvarur, India
2 Department of Dermato-Venereo-Leprology, Government Mohan Kumaramangalam Medical College, Salem, Tamil Nadu, India

Correspondence Address:
Dr. Balaji Govindan
5/32, V. Arumuga Nagar, Alagapuram, Salem - 636 016, Tamil Nadu
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/ijstd.IJSTD_64_17

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Context: Even though condom offers more than 90% protection against human immunodeficiency viral infections (human immunodeficiency virus) and few sexually transmitted infections (STIs), the overall use of condom in India is low. Many studies revealed that the significant barriers for not using condom were lack of privacy in stores, cultural differences, etc. Aims: We intended to find out the reasons for not using condoms in patients attending the STI clinic, by using questionnaire, and had applied machine learning tool to predict those reasons for not using condoms, from the data collected. Subjects and Methods: A questionnaire was administered on 120 patients of age above 10 years attending the STI clinic in a tertiary hospital. From the dataset obtained, we intended to understand if the demographic profile of the candidate could predict the reasons for the avoidance of condoms during sexual activity, by using machine learning algorithm called Support Vector Machine. Statistical Analysis Used: MS Excel worksheet to enter the data and Support Vector Machine algorithm were used for statistical analysis. Results: Respondents were 53% male, 45% female, and 2% transgender. Despite the knowledge of the condoms, 68% of the patients in the study did not use condom. The majority of the patients (41%) stated that condoms were not necessary when they have sexual activity with a known and consistent partner. With machine learning, we found that the prediction accuracy was significantly more than chance (73.47% ±14%) when the feature vectors include only the response to Question 1. Conclusions: Results of the study identify the specific reasons for not using condom and help us in devising specific strategies to promote the condom usage. Our results from machine learning suggest that gender of the respondent is the best predictor in predicting the reason for the nonusage of condom.


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