⚡ Quick Summary
This study conducted a data-driven analysis of suicide patterns in Shiraz City, utilizing data mining and geographical visualization techniques. The findings revealed that 74.18% of suicide cases involved females, with a significant prevalence of drug-related methods, highlighting critical high-risk areas for intervention.
🔍 Key Details
- 📊 Dataset: 923 individuals, 13 features including demographics and suicide methods
- 🧩 Features used: Age, gender, method of suicide, reasons, mental health history
- ⚙️ Technology: SPSS for statistical analysis, ArcGIS for geographical visualization, neural networks and K-NN for prediction
- 🏆 Performance: Neural networks accuracy: 77.3%, K-NN accuracy: 77.4%
🔑 Key Takeaways
- 📊 Majority of cases (74.18%) were females, indicating a gender disparity in suicide rates.
- 💡 Common reasons for suicide included family problems and emotional issues.
- 👩🔬 Most frequent method of suicide was through common therapeutic drugs.
- 🏆 High-risk regions identified include areas 2, 5, 7, 8, and 11 in Shiraz.
- 🤖 Predictive models effectively highlighted vulnerable populations and regions.
- 🌍 Visualization techniques can aid health policymakers in targeted interventions.
- 🆔 Study conducted at Hazrat Ali Asghar Hospital, a specialized center for poisonings.
📚 Background
Suicide remains a pressing public health concern, influenced by a myriad of social, cultural, and demographic factors. Understanding the patterns and predictors of suicide is essential for developing effective prevention strategies. This study aims to leverage modern data analysis techniques to shed light on the suicide landscape in Shiraz City.
🗒️ Study
The research involved a comprehensive analysis of a dataset comprising 923 individuals who had attempted suicide, collected from Hazrat Ali Asghar Hospital. The study focused on various features such as demographic information, methods, and reasons for suicide, employing data mining and geographical visualization to predict and analyze patterns.
📈 Results
After data preprocessing, a total of 883 cases were analyzed. The results indicated a significant female predominance in suicide attempts, with the majority being under 37 years old. The predictive models achieved accuracies of 77.3% for neural networks and 77.4% for K-NN, successfully identifying high-risk areas for drug-related suicides in Shiraz.
🌍 Impact and Implications
The insights gained from this study are crucial for health policymakers. By identifying high-risk areas and understanding the demographics of vulnerable populations, targeted interventions can be developed. The use of geographical visualization in predicting suicide methods offers a powerful tool for prevention, control, and resource optimization in mental health services.
🔮 Conclusion
This study highlights the importance of data-driven approaches in understanding and addressing suicide patterns. The findings underscore the need for tailored interventions, particularly for vulnerable groups, and demonstrate the potential of geographical visualization in enhancing public health strategies. Continued research in this area is essential for effective suicide prevention.
💬 Your comments
What are your thoughts on the findings of this study? How can we better utilize data to prevent suicides in our communities? 💬 Share your insights in the comments below or connect with us on social media:
Data-Driven Analysis and Prediction of Suicide Patterns Using Data Mining and Geographical Visualization in Shiraz City.
Abstract
INTRODUCTION: Suicide is one of the most critical health issues caused by various social, cultural, and demographic factors. The objective of this study was to analyze and predict suicide in Shiraz using visualization and data mining methods.
METHODS AND MATERIALS: The dataset utilized in this research consists of information from 923 individuals and 13 features, including demographic information, address, method of suicide, reason for suicide, history of mental illnesses, employment status, and history of suicide attempts. These data were collected from Hazrat Ali Asghar Hospital in Shiraz, a specialized center for treating poisonings. To improve the modeling accuracy, only data relevant to the city of Shiraz were included, and missing values were replaced using the most/average method. Then, descriptive-statistical analysis was performed to examine the influence of gender, age, and cause of suicide on the method of suicide using SPSS 16 software. ArcGIS software was used for geographical visualization of the 11 regions in Shiraz. In the next stage, neural network and K-NN algorithms were used to predict suicide methods and visualize them on the map using the Orange V3.3 tool.
RESULTS: After preprocessing of data, 883 suicide cases were included in the study, of which 74.18% were females. The majority of the individuals were under 37 years old, and they mostly attempted suicide due to family problems and emotional issues using common therapeutic drugs. In Shiraz, regions 2, 5, 7, 8, and 11 had a relatively higher relative risk than other areas. Additionally, the results of the predictive models of neural networks and K-NN with accuracies of 77.3% and 77.4%, respectively, identified high-risk areas for drug-related suicide in Shiraz.
CONCLUSION AND DISCUSSION: The findings of this study demonstrate that females are more vulnerable than males, and the most common method of suicide is through the use of common therapeutic drugs. By employing visualization in predicting drug-related suicide methods, this study effectively highlighted high-risk areas in Shiraz. Additionally, predicting drug-related suicide methods and visualizing them on a geographic map can help health policymakers in identifying vulnerable individuals and regions and taking appropriate measures for prevention, control, monitoring, evaluation, education, resource optimization, and managing access to therapeutic drugs.
Author: [‘Keshavarz Z’, ‘Saeedinejad S’, ‘Sadat Asmarian N’, ‘Rezaee R’]
Journal: Iran Biomed J
Citation: Keshavarz Z, et al. Data-Driven Analysis and Prediction of Suicide Patterns Using Data Mining and Geographical Visualization in Shiraz City. Data-Driven Analysis and Prediction of Suicide Patterns Using Data Mining and Geographical Visualization in Shiraz City. 2024; 28:19.