MDPI Acta Medica Okayama 2227-9032 10 5 2022 Impact of System and Diagnostic Errors on Medical Litigation Outcomes: Machine Learning-Based Prediction Models 892 EN Norio Yamamoto Department of Epidemiology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University Shintaro Sukegawa Department of Oral and Maxillofacial Surgery, Kagawa Prefectural Central Hospital Takashi Watari General Medicine Center, Shimane University Hospital No prediction models using use conventional logistic models and machine learning exist for medical litigation outcomes involving medical doctors. Using a logistic model and three machine learning models, such as decision tree, random forest, and light-gradient boosting machine (LightGBM), we evaluated the prediction ability for litigation outcomes among medical litigation in Japan. The prediction model with LightGBM had a good predictive ability, with an area under the curve of 0.894 (95% CI; 0.893-0.895) in all patients' data. When evaluating the feature importance using the SHApley Additive exPlanation (SHAP) value, the system error was the most significant predictive factor in all clinical settings for medical doctors' loss in lawsuits. The other predictive factors were diagnostic error in outpatient settings, facility size in inpatients, and procedures or surgery settings. Our prediction model is useful for estimating medical litigation outcomes. No potential conflict of interest relevant to this article was reported.