Abstract :

Obsessive Compulsive Disorder (OCD) Is A Chronic Mental Health Condition Characterized By Intrusive Thoughts (obsessions) And Repetitive Behaviors (compulsions), Significantly Affecting An Individual’s Quality Of Life. Effective Treatment Often Involves A Combination Of Psychotherapy And Pharmacological Interventions, Such As Selective Serotonin Reuptake Inhibitors (SSRIs). However, Identifying The Most Suitable Medication And Dosage For Each Patient Remains A Complex And Time-consuming Process Due To Variations In Individual Responses. This Study Explores The Application Of Machine Learning Techniques To Improve The Prediction And Optimization Of OCD Medication Outcomes. The Proposed System Utilizes Patient Data Including Demographic Information, Clinical History, Symptom Severity Scores, Medication Records, And Treatment Responses. Data Preprocessing Techniques Such As Cleaning, Normalization, And Feature Selection Are Applied To Enhance Data Quality. Machine Learning Algorithms Such As Logistic Regression, Random Forest, Support Vector Machines (SVM), And Artificial Neural Networks (ANN) Are Used To Predict Treatment Effectiveness And Recommend Personalized Medication Plans. The System Aims To Identify Patterns In Patient Responses To Different Medications, Enabling Clinicians To Make More Informed Decisions. Experimental Results Indicate That Machine Learning Models Can Effectively Predict Medication Outcomes And Improve Treatment Accuracy. Ensemble Models Such As Random Forest Demonstrate Higher Performance Due To Their Ability To Handle Complex And Nonlinear Relationships. However, Challenges Such As Limited Datasets, Data Privacy Concerns, And Model Interpretability Remain. Overall, This Study Highlights The Potential Of Machine Learning In Enhancing Personalized Treatment Strategies For OCD, Leading To Improved Patient Outcomes And More Efficient Healthcare Delivery.

Published:

08-4-2026

Issue:

Vol. 26 No. 4 (2026)

Page Nos:

1878-1884

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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