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Prediction of Acute Organophosphate Poisoning Severity Using Machine Learning Techniques by Sayed Masoud Hosseini; Mitra Rahimi; Mohammad Reza Afrash; Pardis Ziaeefar; Parsa Yousefzadeh; Sanaz Pashapour; Peyman Erfan Talab Evini; Babak Mostafazadeh; Shahin Shadnia is a Environmental Science article available to read on EtoBox.

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Poisoning with organophosphate compounds is a significant public health risk, especially in developing countries. Considering the importance of early and accurate prediction of organophosphate poisoning prognosis, the aim of this study was to develop a machine learning-based prediction model to predict the severity of organophosphate poisoning. The data of patients with organophosphate poisoning were retrospectively extracted and split into training and test sets in a ratio of 70:30. The feature selection was done by least absolute shrinkage and selection operator method. Selected features were fed into five machine learning techniques, including Histogram Boosting Gradient, eXtreme Gradient Boosting, K-Nearest Neighborhood, Support Vector Machine (SVM) (kernel = linear), and Random Forest. The Scikit-learn library in Python programming language was used to implement the models. Finally, the performance of developed models was measured using ten-fold cross-validation methods and some evaluation criteria with 95 % confidence intervals. A total of 1237 patients were used to train and test the machine learning models. According to the criteria determining severe organophosphate poison

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Author

Sayed Masoud Hosseini; Mitra Rahimi; Mohammad Reza Afrash; Pardis Ziaeefar; Parsa Yousefzadeh; Sanaz Pashapour; Peyman Erfan Talab Evini; Babak Mostafazadeh; Shahin Shadnia

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Environmental Science (Life Sciences)