Machine learning analysis of non-marital sexual violence in India

Abstract

Background: Machine learning techniques can explore low prevalence data to offer insight into identification of factors associated with non-marital sexual violence (NMSV). NMSV in India is a health and human rights concern that disproportionately affects adolescents, is under-reported, and not well understood or addressed in the country. Methods: We applied machine learning methods to retrospective cross-sectional data from India's nationallyrepresentative National Family Health Survey 4, a demographic and health study conducted in 2015À16, which offers 4000+ variables as potential independent variables. We used Least Absolute Shrinkage and Selection Operator (lasso) or L-1 regularized logistic regression models as well as L-2 regularized logistic regression or ridge models; we conducted an iterative thematic analysis (ITA) of variables generated from a series of regularized models. Findings: Thematic analysis of regularized models highlight that past exposure to violence was most predictive of NMSV, followed by geography, sexual behavior, and poor sexual and reproductive health knowledge. After these, indicators largely related to resources and autonomy (e.g., access to health services, and income generating) were associated with NMSV. Exploratory analysis with the subsample of never married adolescents 15À19 years old, a population with higher representation of recent NMSV, further emphasized the role of wealth and mobility as key correlates of NMSV, along with poor HIV knowledge, tobacco use, higher fertility preferences, and attitudes accepting of marital violence. Interpretation: Findings indicate the validity of machine learning with iterative theme analysis (ITA) to identify factors associated with violence. Findings were consistent with prior work demonstrating associations between NMSV and other violence experiences, but also showed novel correlates such as lower SRH knowledge and service utilization and, for girls, norms and preferences suggesting more restrictive gender norms. Sexual and reproductive health, gender equity and safety focused interventions are important for addressing NMSV in India, particularly for adolescents.

Machine Learning Theory in Building Energy Modeling and Optimization: A Bibliometric Analysis

“…Keywords were used to identify topics with high relevance to machine learning. The articles about machine learning in building energy modeling were imported into the software, the associations among them were exhibited [43] . Figure 16 shows that the number of sections has increased and the cases that are related to each other are clearly identified.…”

Adolescent Sexual Behavior in Rural Central India: Challenges and Interventions

“…Poverty also affects their education, and many people have to leave school to support their families. Because of their lack of financial security and education, they are more likely to engage in risky sexual acts [ 16 ].…”

Machine Learning Applied to Gender Violence: A Systematic Mapping Study

“…For this reason, in [49], ML was used to explore a range of social, demographic, and health factors provided in a large national survey of 66,013 women aged 15-49 years who were ever married to determine which of these factors are associated with marital sexual violence (MSV). Similarly, in [50], a study was conducted applying regularized regression models to identify factors associated with non-marital sexual violence (NMSV), having as a source of information the national family health survey conducted between 2015 and 2016, where having previously suffered this type of violence was identified as the main risk factor, followed by geography, sexual behavior, and low knowledge about sexual and reproductive health (SRH). Moreover, an important factor to consider is the low number of women that seek help.…”

“…In the case of gender-based violence, multiple articles propose different objectives, such as proposing new systems or improving current systems [5], comparing different algorithms to find the one with the best performance for a given problem [8], determining situations of danger [41], predicting the number of reports of gender violence [44], minimizing the recidivism of the aggressor [48], determining factors associated with sexual violence [49], and determining the appropriate level of protection for a victim [5]. These are some of the objectives that can be established when addressing gender-based violence with an ML approach [6], [7], [40], [42], [43], [45], [46], [47], [49], [50], [51].…”