Logo Lanfrica
  • Accueil
  • Atlas
  • Analyses
  • Documentation
  • Sign in

© 2026 Lanfrica. Tous droits réservés. Tous les droits d'auteur des ressources affichées sur le site Web Lanfrica appartiennent aux détenteurs de droits d'auteur d'origine, sauf indication contraire explicite.

A Hybrid Machine Learning Model for Predicting Surgical Procedure Duration: Integrating Random Forest and K-Means Clustering

Domaine:

healthcare

Type de record:

papermodel
Créateur:
BRAHMI, AmiraBedBHAR LAYEB, SafaAis
Éditeur:
LR-UniCen
Éditeur:
CCSDIEEE
Hôte:avatar
International audience Efficient operating room (OR) management depends on the accurate prediction of surgical procedure durations to improve scheduling, enhance patient outcomes, and reduce operational costs. This study presents a hybrid machine learning model that combines Random Forest and K-Means clustering to predict the duration of cholecystectomy procedures. The model is trained using real-world data from the digestive surgery department at Mahmoud El Matri Hospital in Tunis, Tunisia, incorporating patient demographics, surgeon experience, and other contextual factors. Synthetic data generation was also applied to reinforce model reliability. The proposed approach achieved strong performance, with a root mean square error (RMSE) of 0.45 minutes, a mean absolute error (MAE) of 0.36 minutes, and a coefficient of determination (R2) of 0.99. Comparative analysis with individual models such as Random Forest, K-Means, decision trees, and linear regression confirms the hybrid model’s superior predictive capability. These results demonstrate the potential of the proposed hybrid model as a practical tool for optimizing OR scheduling and improving healthcare resource management.

Visit

imt-mines-albi.hal.science

Tags

Synthetic dataRandom forestsDecision treesResource managementData modelsPredictive modelsSurgeryLinear regressionHospitalsAccuracy+1

Similaires

Okes2024/A-hybrid-AI-model-integrating-LSTM-XGBoost-and-K-means-for-interpretable-prediction-_-clusteringA Hybrid Extended Cox-frailty and Random Forest Framework for Prostate Cancer Survival: Integrating Clinical Interpretability with Machine Learning PerformanceAn embedded segmental K-means model for unsupervised segmentation and clustering of speechPredicting terrestrial heat flow in Egypt using random forest regression: a machine learning approachIntegrating Remote Sensing and Machine Learning for Crop Classification in Latur Using Google Earth Engine and Random ForestA Hybrid Machine Learning Model for Predicting Diseases in Coffee Production: A case study from Kenya

Okes2024/A-hybrid-AI-model-integrating-LSTM-XGBoost-and-K-means-for-interpretable-prediction-_-clustering

A Hybrid Machine Learning Framework for Water Quality Assessment and Contamination Clustering in the

A Hybrid Extended Cox-frailty and Random Forest Framework for Prostate Cancer Survival: Integrating Clinical Interpretability with Machine Learning Performance

Prostate cancer risk stratification in low-resource clinical settings requires survival-prediction t

An embedded segmental K-means model for unsupervised segmentation and clustering of speech

Unsupervised segmentation and clustering of unlabelled speech are core problems in zero-resource spe

Predicting terrestrial heat flow in Egypt using random forest regression: a machine learning approach

Abstract This work aims to create a machine-learning model that can contribute to a comprehensive un

Integrating Remote Sensing and Machine Learning for Crop Classification in Latur Using Google Earth Engine and Random Forest

A Hybrid Machine Learning Model for Predicting Diseases in Coffee Production: A case study from Kenya

In Kenya coffee farming faces various challenges, which include widespread pests and diseases. These