The confusion matrix for GBM, XGBoost and LGBM.

<div><p>The increasing complexity of diagnostic imaging often leads to misinterpretations and diagnostic errors, particularly in critical conditions such as pneumothorax. This study addresses the pressing need for improved diagnostic accuracy in CT scans by developing an intelligent mode...

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Main Author:	Hanieh Alimiri Dehbaghi (20405015) (author)
Other Authors:	Karim Khoshgard (20405018) (author), Hamid Sharini (20405021) (author), Samira Jafari Khairabadi (20405024) (author), Farhad Naleini (20405027) (author)
Published:	2024
Subjects:	Neuroscience Cancer Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified various evaluation criteria receiver operating characteristic machine learning techniques lgbm )&# 8212 gradient tree boosting gradient boosting machine enhance patient care based machine learning 100 %, respectively mitigate diagnostic errors machine learning models demonstrated strong performance calculated evaluation criteria improved diagnostic accuracy gbm model outperformed leverages radiomics features diagnostic errors model achieved intelligent model xlink "> supporting radiologists significant potential research aims radiomics features pressing need positive cases medical records matlab software light gbm increasing complexity highest performance f1 score expedite evaluations data used ct scans critical conditions collected images auc ), 55 %. 29 %. 175 patients
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The confusion matrix for GBM, XGBoost and LGBM.

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