This project analyzes the "OCD Patient Dataset: Demographics & Clinical Data" to uncover patterns among 1500 individuals diagnosed with OCD. It focuses on demographic details like age, gender, and education, alongside clinical and risk factors such as symptom severity (Y-BOCS/Yale Brown Obsessive-Compulsive Scale scores), co-occurring conditions, and treatment methods. By exploring obsession and compulsion types, symptom duration, and the role of family history, the analysis provides insights into common risk factors and trends. The findings aim to better understand OCD's diverse presentations and support research and clinical efforts.

Kaggle : OCD Patient Dataset: Demographics & Clinical Data

To facilitate data management and analysis, PostgreSQL database was used to store the dataset. SQL provides a robust and efficient way to query and manipulate the data. The database schema has been designed to ensure proper organization and ease of use. The structure of the database enables seamless integration with various data visualization tools.

Performed preliminary analysis in PostgreSQL to answer important questions about the distribution of OCD symptom severity across different age groups, the correlation between family history and symptom severity, and the impact of medications on patient outcomes.

Developed a visually engaging dashboard to present the key insights discovered in PostgreSQL, providing clear and actionable data visualizations.

A statistical summary of OCD patients featuring line charts tracking diagnosis dates, KPI cards highlighting obsession and compulsion scores with key aggregates, and a distribution chart illustrating the percentage of patients across various OCD severity levels.

Insights into the Education level, Marital status, Ethicity and Gender of the OCD Patients.

Highlights the Risk Factors and Common causes and types of Obsession-Compulsion disorders.

View the dashboard preview on my NovyPro profile.