2026-10-11T15:56:05Z https://www.scidb.cn/oai

10.57760/sciencedb.j00253.05088 2026-05-29T16:45:46Z

2026-05-29 Diagnostic Prediction Model for Encephalitis and Intracranial Malignant Tumors Based on Common Blood Test Indicators and Random Forest Algorithm doi:10.57760/sciencedb.j00253.05088 en Objective Establish a diagnostic prediction model for encephalitis and intracranial malignant tumors using common blood test indicators and the random forest algorithm, providing a non-invasive and efficient auxiliary diagnostic tool for clinical practice.Methods A retrospective collection was conducted on 134 patients diagnosed with encephalitis and 153 patients diagnosed with intracranial malignant tumors from November 2022 to March 2024. Age, sex, and 61 common blood test indicators prior to treatment were collected for all patients. Key feature variables were screened using LASSO regression, and a predictive model was constructed using the random forest algorithm. Model parameters were optimized using 10-fold cross-validation and grid search. Model performance was evaluated using accuracy, precision, recall, F1-score, and the area under the ROC curve (AUC). Feature importance was analyzed using SHAP values.Results The LASSO regression ultimately identified 14 key features. The constructed random forest model demonstrated good predictive performance on the test set, with an AUC of 0.9512, accuracy of 0.9080, recall of 0.8261, and F1 score of 0.9048. SHAP analysis revealed that C‑reactive protein (CRP) contributed the most to the model, followed by lymphocyte (LYM) and phosphorus (P).Conclusion The random forest model based on common blood indicators can effectively differentiate encephalitis from malignant tumors, exhibiting high clinical practical value, with CRP being a particularly important predictor. Encephalitis; Intracranial Malignant Tumors; Blood Test Indicators; Random Forest Algorithm; Prediction Model Ju.GAO Jie.FENG Yanhong.GAO PUBLIC https://creativecommons.org/publicdomain/zero/1.0/ dataset Science Data Bank