Volume 11 - Volume 11
Study of Interpretability in ML Algorithms for Disease Prognosis
Abstract
Disease prognosis plays an important role in healthcare. Diagnosing disease at an early stage is
crucial to provide treatment to the patient at the earliest in order to save his/her life or to at least
reduce the severity of the disease. Application of Machine Learning algorithms is a promising area
for the early and accurate diagnosis of chronic diseases. The black-box approach of Machine
Learning models has been circumvented by providing different Interpretability methods. The
importance of interpretability in health care field especially while taking decisions in life threatening
diseases is crucial. Interpretable model increases the confidence of a medical practitioner in taking
decisions. This paper gives an insight to the importance of explanations as well as the interpretability
methods applied to different machine learning and deep learning models developed in recent years.
Paper Details
PaperID: 2500
Author's Name: P. Archana Menon and Dr.R. Gunasundari
Volume: Volume 11
Issues: Volume 11
Keywords: Interpretability, Explainability, Disease Prognosis, Machine Learning, Deep Learning, Blackbox, Whitebox, Visual Representation.
Year: 2021
Month: August
Pages: 4735-4749