A regression analysis for predicting surgical complications

Ahmad Madani, Anis Zahirah and Mohamad Pauzi, Nur Hanani and Ahmad Radzi, Nur Iwana and Wan Tarmizi, Wan Nurul Adibah and Wani, Sharyar and Olowolayemo, Akeem (2023) A regression analysis for predicting surgical complications. International Journal on Perceptive and Cognitive Computing (IJPCC), 9 (1). pp. 95-100. E-ISSN 2462-229X

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Abstract

A surgical complication is any undesirable and unexpected result of an operation. Surgical complications could be fatal to a patient if they are not detected earlier. One of the factors that could affect the severity of the complication is the time between a patient's diagnosis and the surgery. The patient might be at risk if the doctor misdiagnoses them or concludes that the patient has no severe symptoms. This paper aims to study the correlation between post-surgical conditions & time duration with possible surgical complications. Using regression analysis, the research intends to evaluate predictive possibilities of early discovery of these complications.The results reveal that the Gradient Boosting Regressor performs with minimal error rate and predicts almost all complications in line with the original data, measured across MAE, RMSE and R2 with scores of 0.07, 0.11 and 0.98 respectively.In comparison to Random Forest Regressor and Decision Tree Regressor, Gradient Boosting Regressor performs 70-80% efficiently across the three major aforementioned metrics on average.Thus, presenting itselfas a valuable tool for finding the correlations in surgical data and early intervention of possible surgical complications.

Item Type:	Article (Journal)
Uncontrolled Keywords:	surgicalcomplication, medical, diagnosis time, predictive modelling, regression
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Information and Communication Technology > Department of Computer Science Kulliyyah of Information and Communication Technology > Department of Computer Science
Depositing User:	Dr. Sharyar Wani
Date Deposited:	06 May 2025 10:09
Last Modified:	06 May 2025 10:09
URI:	http://irep.iium.edu.my/id/eprint/120786

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