• H.A.ARAVINDA
• PRATHYAJ MANTHA
• SHILPA DAS

The ordering of Magnetic resonance imaging (MRI) brain scans following American College of Radiology (ACR) guidelines showed a higher percentage of brain abnormalities compared to scans that do not. As the process of manually labelling patient orders obtained from a local tertiary hospital in accordance to ACR guidelines is intensive and time consuming, this study aims to develop predictive machine learning models; Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF) and XGBoost (XGB), to automate the classification process through text mining methods and derive insights that are useful for future clinical decision-making and resource optimization. Using 1,924 observations as the labelled training data, RF and XGB were found to be the best performing robust models with ROC values of 0.9459 and 0.9508 respectively on the validation set (481 observations). Further exploration into the interpretability of black-box algorithms using the model agnostic LIME (Local Interpretable Model-Agnostic Explanations) framework was used to generate further insights for decisions made using a separate XGB model with respect to individual patients. The LIME framework is a significant first step towards the development of a comprehensive decision support system for patient-level decisions in the ordering of MRI scans.

Magnetic resonance imaging,machine learning models,text mining

"Automation of Classification of MRI Brain Scans: An Emerging Trend ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.508-513, June-2019, Available :http://www.jetir.org/papers/JETIR1906655.pdf

"Automation of Classification of MRI Brain Scans: An Emerging Trend ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp508-513, June-2019, Available at : http://www.jetir.org/papers/JETIR1906655.pdf