⚡ Quick Summary
This study utilized the decision tree method to identify key factors associated with Multiple Sclerosis (MS), revealing that female gender, family history, stress, vitamin D deficiency, and infectious diseases significantly influence the disease’s onset and progression.
🔍 Key Details
- 📊 Dataset: 317 individuals (188 MS patients, 128 healthy controls)
- 🧩 Features analyzed: Gender, age, family history, trauma, bowel disease, rheumatism, infectious disease, stress, depression, anxiety, migration, vitamin D deficiency, smoking
- ⚙️ Technology: Decision tree method and gain ratio index
- 🏆 Study duration: May 2016 to September 2017
🔑 Key Takeaways
- 👩⚕️ Female gender is a significant risk factor for MS.
- 🧬 Family history of MS increases susceptibility.
- 😟 Stress is a notable environmental factor linked to MS.
- ☀️ Vitamin D deficiency is associated with higher MS risk.
- 🦠 Infectious diseases may play a role in MS development.
- 📈 Decision tree analysis effectively identifies critical factors influencing MS.
- 🌍 Study conducted at Kermanshah University of Medical Sciences.
- 📅 Published in: Iran Biomed J, 2024.
📚 Background
Multiple Sclerosis (MS) is a complex neurological disorder characterized by the destruction of the insulating covering of nerve cells. It is the third leading cause of disability globally, following trauma and rheumatism. Despite extensive research, the exact causes of MS remain elusive, with a combination of genetic and environmental factors believed to contribute to its onset.
🗒️ Study
This analytical study aimed to explore various factors associated with MS using a dataset obtained from the health registration system at Kermanshah University of Medical Sciences. The research involved 317 individuals, including 188 diagnosed with MS and 128 healthy controls, and utilized magnetic resonance imaging for accurate disease diagnosis. The analysis was conducted using R 4.0.3 software, focusing on multiple variables that could influence the disease.
📈 Results
The findings indicated that several factors significantly affect the likelihood of developing MS. The most impactful were female gender (21.3), family history of MS (18.94), history of stress (17.2), vitamin D deficiency (15.78), and infectious disease (15.21). These results underscore the importance of understanding both genetic and environmental influences on MS.
🌍 Impact and Implications
The implications of this study are profound. By identifying critical factors associated with MS, healthcare providers can better inform patients about potential risks and preventive measures. Increased awareness of the role of stress, vitamin D levels, and infectious diseases can lead to improved management strategies and potentially slow the progression of MS symptoms. This research highlights the need for ongoing education and support for individuals at risk.
🔮 Conclusion
This study illustrates the power of machine learning techniques, such as decision trees, in uncovering significant factors related to complex diseases like MS. By focusing on environmental and genetic influences, we can enhance our understanding of MS and improve patient care. Continued research in this area is essential for developing effective prevention and treatment strategies.
💬 Your comments
What are your thoughts on the findings of this study regarding Multiple Sclerosis? We invite you to share your insights and engage in a discussion! 💬 Leave your comments below or connect with us on social media:
Identifying the Most Important Factors Associated with Multiple Sclerosis Using the Decision Tree Method.
Abstract
INTRODUCTION: Multiple sclerosis (MS) is a neurological disease that destroys the insulating covering of nerve cells. It is the third leading cause of disability after trauma and rheumatism. Studies have shown that the main cause of MS is not fully understood, and it is believed to be influenced by a combination of unknown genetic and environmental factors. Considering the ability of machine learning techniques, such as decision trees, to identify significant factors related to diseases, this study aimed to investigate various factors related to MS using the decision tree method.
METHODS AND MATERIALS: This analytical and modeling study was conducted using the MS disease dataset. The data were obtained from the health registration system at Kermanshah University of Medical Sciences. A total of 317 individuals were studied from May 2016 to September 2017, comprising 188 individuals diagnosed with MS and 128 healthy controls. Magnetic resonance imaging was utilized for disease diagnosis. The data were processed in the R 4.0.3 software environment. The variables analyzed included gender, age, family history of MS, trauma, bowel disease, rheumatism, infectious disease, stress, depression, anxiety, migration, vitamin D deficiency, and smoking. The decision tree method and the gain ratio index were employed to assess the significance of factors influencing MS disease.
RESULTS: According to the results, female gender, family history of MS, history of stress, vitamin D deficiency, and infectious disease with indices of 21.3, 18.94, 17.2, 15.78, and 15.21 were among the factors affecting MS disease.
CONCLUSION AND DISCUSSION: Three significant environmental factors associated with MS include a history of stress, a deficiency in vitamin D, and exposure to infectious diseases. Therefore, both individuals and service providers need to be aware of these factors to prevent the progression and exacerbation of its symptoms.
Author: [‘Manochehri S’, ‘Manochehri Z’, ‘Lorestani T’, ‘Zamani M’]
Journal: Iran Biomed J
Citation: Manochehri S, et al. Identifying the Most Important Factors Associated with Multiple Sclerosis Using the Decision Tree Method. Identifying the Most Important Factors Associated with Multiple Sclerosis Using the Decision Tree Method. 2024; 28:52.