Monitoring multistage healthcare processes using state space models and a machine learning based framework

Yeganeh, Ali; Johannssen, Arne; Chukhrova, Nataliya; Rasouli, Mohammad

doi:10.1016/j.artmed.2024.102826

Cited by 4 publications

(1 citation statement)

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“…The goal of the Yeganeh et al, [17] is to improve early detection of abnormalities in healthcare processes by introducing a Multistage Process Monitoring (MPM) tool designed specifically for healthcare data. The tool enhances detection capacities through the integration of machine learning approaches with statistical control charts.…”

Section: Literature Reviewmentioning

confidence: 99%

Comparative Analysis of Machine Learning Algorithms for Liver Disease Prediction: SVM, Logistic Regression, and Decision Tree

Modhugu,

Ponnusamy

2024

Asian J. Res. Com. Sci.

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This study compares Support Vector Machine (SVM), Logistic Regression, and Decision Tree algorithms for liver disease prediction using a dataset sourced from Kaggle, comprising 20,000 training records and approximately 1,000 test records. The research evaluates the algorithms based on performance metrics, including accuracy, precision, recall, and F1-score. SVM emerged as the most effective model with an accuracy of 85%, followed by Logistic Regression with 82% and Decision Tree with 79%. The findings underscore the significance of algorithm selection in healthcare applications and highlight SVM's potential for early detection and intervention in liver disease cases, paving the way for improved patient outcomes and healthcare management. Future work will focus on refining the algorithms and validating the results with larger and more diverse datasets to enhance predictive accuracy and robustness further.

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Section: Literature Reviewmentioning

confidence: 99%