A method for predicting mortality in acute mesenteric ischemia: Machine learning

• Published in: Turkish Journal of Trauma and Emergency Surgery Vol. 30; no. 7; pp. 487 - 492
• Main Author: Harmantepe, Ahmet Tarık
• Format: Journal Article
• Language: English
• Published: Turkey KARE Publishing 01.07.2024; Kare Publishing
• Subjects: Acute Disease; Adult; Aged; Aged, 80 and over; Algorithms; Analysis; Artificial intelligence; C-reactive protein; Confidence intervals; Creatinine; Female; Health aspects; Hospital Mortality; Hospitalization; Humans; Information management; Ischemia; Lactates; Machine Learning; Male; Medical research; Medicine, Experimental; Mesenteric Ischemia - mortality; Methods; Middle Aged; Mortality; Original; Predictive Value of Tests; Prognosis; Regression analysis; Risk factors; Success
• ISSN: 1306-696X; 1307-7945
• DOI: 10.14744/tjtes.2024.48074

This study aimed to develop and validate an artificial intelligence model using machine learning (ML) to predict hospital mortality in patients with acute mesenteric ischemia (AMI). A total of 122 patients diagnosed with AMI at Sakarya University Training and Research Hospital between January 2011 and June 2023 were included in the study. These patients were divided into a training cohort (n=97) and a validation cohort (n=25), and further categorized as survivors and non-survivors during hospitalization. Serum-based laboratory results served as features. Hyperfeatures were eliminated using Recursive Feature Elimination (RFE) in Python to optimize outcomes. ML algorithms and data analyses were performed using Python (version 3.7). Of the patients, 56.5% were male (n=69) and 43.5% were female (n=53). The mean age was 71.9 years (range 39-94 years). The mortality rate during hospitalization was 50% (n=61). To achieve optimal results, the model incorporated features such as age, red cell distribution width (RDW), C-reactive protein (CRP), D-dimer, lactate, globulin, and creatinine. Success rates in test data were as follows: logistic regression (LG), 80%; random forest (RF), 60%; k-nearest neighbor (KN), 52%; multilayer perceptron (MLP), 72%; and support vector classifier (SVC), 84%. A voting classifier (VC), aggregating votes from all models, achieved an 84% success rate. Among the models, SVC (sensitivity 1.0, specificity 0.77, area under the curve (AUC) 0.90, Confidence Interval (95%): (0.83-0.84)) and VC (sensitivity 1.0, specificity 0.77, AUC 0.88, Confidence Interval (95%): (0.83-0.84)) were noted for their effectiveness. Independent risk factors for mortality were identified in patients with AMI. An efficient and rapid method using various ML models to predict mortality has been developed.