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Journals / Avrupa Bilim ve Teknoloji Dergisi / 2022 / Sayı: 44

Violence Detection with Machine Learning: A Sociodemographic Approach

Tolga ENSARİ
Betul ENSARİ
Mustafa DAĞTEKİN
Pages
104–107
Publication Date
2022-12-31
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

According to our analysis, the decision tree (DT) performs the best performance in terms of accuracy.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This study suggests that by implementing machine learning methods on a sociodemographic data set can be helpful in preventing domestic violence.
📊
Key Findings & Data:We provide the comparison of the classifiers with precision, recall, F1 score, and accuracy performance measures.
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Conclusion & Impact:According to our analysis, the decision tree (DT) performs the best performance in terms of accuracy.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Violence detection · machine learning · sociodemographic

Abstract

This study suggests that by implementing machine learning methods on a sociodemographic data set can be helpful in preventing domestic violence. This approach is important in predicting high-risk factors that an offender may cause and it offers treatment, and financial or mental health aids in order to prevent domestic violence. In this sense, this proposal is critical at a personal and social level in creating a secure and healthy environment as well as empowering an equal society. In our study, we use k-nearest neighbor (k-nn), support vector machine (SVM), decision tree (DT), and Gaussian Naive Bayes (GNB) machine learning algorithms for the prediction analysis. We provide the comparison of the classifiers with precision, recall, F1 score, and accuracy performance measures. According to our analysis, the decision tree (DT) performs the best performance in terms of accuracy.

Özet

This study suggests that by implementing machine learning methods on a sociodemographic data set can be helpful in preventing domestic violence. This approach is important in predicting high-risk factors that an offender may cause and it offers treatment, and financial or mental health aids in order to prevent domestic violence. In this sense, this proposal is critical at a personal and social level in creating a secure and healthy environment as well as empowering an equal society. In our study, we use k-nearest neighbor (k-nn), support vector machine (SVM), decision tree (DT), and Gaussian Naive Bayes (GNB) machine learning algorithms for the prediction analysis. We provide the comparison of the classifiers with precision, recall, F1 score, and accuracy performance measures. According to our analysis, the decision tree (DT) performs the best performance in terms of accuracy.

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Cite This Article

ENSARİ, T., ENSARİ, B., DAĞTEKİN, M. (2022). Violence Detection with Machine Learning: A Sociodemographic Approach. Avrupa Bilim ve Teknoloji Dergisi, Sayı: 44, 104–107. https://ajindex.com/articles/878835

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References (8)

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