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Report on self-assessment using Python, AI, and machine learning to predict patient readiness for spinal fusion surgery. Did the decision tree recommend surgery? Case presentation: The case of a 79-year-old retired psychologist (the author) with spinal stenosis, a collapsed L4-L5 disk, and crushed exit spinal nerves is explored. Methods: A boosted decision tree was used for prediction, supported by logistic regression and path analysis. Synthetic data was used alongside real patient data to add variability to the dataset. Findings: In this study, patient responses to a questionnaire were tested to determine if spine fusion surgery would be recommended. The results are limited by single-case and synthetic data. The model consists of a unique patient data array. Python, AI, and machine learning generated a self-assessment approach that offers patients and healthcare professionals an effective prediction tool. Significance: Annually, approximately 450,000 patients undergo spinal surgery following prolonged back pain. Self-assessment is a tool for personal decision making. It adds to a collaborative approach with healthcare providers. Wearable sensors to record spinal disk and nerve pain would be beneficial. Conclusion: The case demonstrates the efficacy of synthetic data in predictive modeling, while acknowledging the limitations in generalizing the findings to broader patient populations without real-world data.