A Novel and Effective Multi-Class Classification Method for Imbalanced Medical Transcriptions

Abstract

Natural Language Processing (NLP) techniques are used to glean information from Electronic Health Records (EHR) for identifying patients with unique clinical characteristics and defining phenotypes. The classification of imbalanced datasets is also one of the vital concerns in medical diagnosis. We built an improved framework for automating the multi-class classification of imbalanced medical transcriptions [1] into 40 medical specialties, by creating a set of important phenotypes/features. We implemented and tested five machine learning models out of which Random Forest Classifier has achieved the highest performance of 0.99 F1 score (precision 0.99, recall 0.99) and roc-auc-score of 0.99 on test data.

KEYWORDS:

• Class imbalance
• Classification
• Feature extraction
• Machine learning
• Natural language processing
• SMOTE-ENN

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Priti Bhardwaj

Priti Bhardwaj is a research scholar in the department of information technology, Indira Gandhi Delhi Technical University for Women, New Delhi. Her research interests include machine learning, healthcare analytics, and natural language processing. E-mail: [email protected]

Niyati Baliyan

Niyati Baliyan is an assistant professor in the department of computer engineering, National Institute of Technology Kurukshetra, Haryana. She received a doctorate of philosophy from the computer science department, Indian Institute of Technology (IIT) in Roorkee, India. Her research interests include knowledge engineering, machine learning, healthcare analytics, recommender systems, information security, and natural language processing. Corresponding author. E-mail: [email protected]