Identifying Early Signs of Bipolar Disorder Risk by Food Habit Analysis in Forensic Using Machine Learning

• Published in: 2024 International Conference on Communication, Computer Sciences and Engineering (IC3SE) pp. 1 - 5
• Main Authors: Kumar, Mandeep, Sirohi, Ramesh, Kaushik, Deepa, Gulhane, Monali, Khare, Neerav, Vats, Sharvin
• Format: Conference Proceeding
• Language: English
• Published: IEEE 09.05.2024
• Subjects: Analytical models; Bipolar; Data visualization; Disorder; Eating Habits; Error analysis; Forensics; Linear Regression; Machine learning; Mental health; Predictive models; SVM
• DOI: 10.1109/IC3SE62002.2024.10593552

The recent study of the forensic science explains that there are several reasons for the person involving into the criminal acts. One the reason for the person involving in the criminal acts is bipolar disorder. The proposed strategy makes it simpler to pre-process and improves data interpretation by using descriptive statistics and visualization approaches to acquire insights into the dataset characteristics from datasets gathered from Kaggle with various mental health information. The model's primary technique is the use of regression techniques, such as conventional high-dimensional modelling for predictive analysis. The comparison study shows that the high-dimensional model of regression performed better, showing reduced error rates and higher scores for R2, which enhanced prediction. Research on the connection between eating habits and the eventual outcome of bipolar illness not only demonstrates validity but also suggests a possible link between eating habits and depressed outcomes.