biostudies-literatureUnknown25(1)Yap XV<h4>Background</h4>Spontaneous intracerebral hemorrhage (SICH) is a devastating condition that significantly contributes to high mortality rates. This study aims to construct a mortality prediction model for patients with SICH using four various artificial intelligence (AI) machine learning algorithms.<h4>Method</h4>A retrospective analysis was conducted on electronic medical records of SICH patients aged 20 and above, admitted to Chi Mei Medical Center's intensive care unit between January 2016 and December 2021. The study utilized 37 features related to mortality. Predictive models were developed using logistic regression, Random forest, LightGBM, XGBoost, and Multi-layer Perceptron (MLP), with assessments of feature importance, and Area under the curve (AUC).<h4>Results</h4>A total of 1BMC medical informatics and decision making149https://www.ebi.ac.uk/biostudies/studies/S-EPMC11951645biostudies-literatureDeveloping a high-performance AI model for spontaneous intracerebral hemorrhage mortality prediction using machine learning in ICU settings.PMC11951645Tu KCChen CJLiu CFEric Nya TTKuo CLChen NCWang CCYap XVfalseDeveloping a high-performance AI model for spontaneous intracerebral hemorrhage mortality prediction using machine learning in ICU settings.<h4>Background</h4>Spontaneous intracerebral hemorrhage (SICH) is a devastating condition that significantly contributes to high mortality rates. This study aims to construct a mortality prediction model for patients with SICH using four various artificial intelligence (AI) machine learning algorithms.<h4>Method</h4>A retrospective analysis was conducted on electronic medical records of SICH patients aged 20 and above, admitted to Chi Mei Medical Center's intensive care unit between January 2016 and December 2021. The study utilized 37 features related to mortality. Predictive models were developed using logistic regression, Random forest, LightGBM, XGBoost, and Multi-layer Perceptron (MLP), with assessments of feature importance, and Area under the curve (AUC).<h4>Results</h4>A total of 12025-01-01T00:00:00Z2025 Mar2026-07-16T20:24:10.963Z2025-06-25T03:04:25.625ZS-EPMC119516454015593510.1186/s12911-025-02984-y