Local Interpretability of Calibrated Prediction Models: A Case of Type 2 Diabetes Mellitus Screening Test

06/02/2020

∙

Machine Learning (ML) models are often complex and difficult to interpret due to their 'black-box' characteristics. Interpretability of a ML model is usually defined as the degree to which a human can understand the cause of decisions reached by a ML model. Interpretability is of extremely high importance in many fields of healthcare due to high levels of risk related to decisions based on ML models. Calibration of the ML model outputs is another issue often overlooked in the application of ML models in practice. This paper represents an early work in examination of prediction model calibration impact on the interpretability of the results. We present a use case of a patient in diabetes screening prediction scenario and visualize results using three different techniques to demonstrate the differences between calibrated and uncalibrated regularized regression model.

READ FULL TEXT

Local Interpretability of Calibrated Prediction Models: A Case of Type 2 Diabetes Mellitus Screening Test

Interpretability of machine learning based prediction models in healthcare

ML Interpretability: Simple Isn't Easy

Monitoring machine learning (ML)-based risk prediction algorithms in the presence of confounding medical interventions

Understanding Interpretability by generalized distillation in Supervised Classification

Oversampling Higher-Performing Minorities During Machine Learning Model Training Reduces Adverse Impact Slightly but Also Reduces Model Accuracy

Interpretability, Then What? Editing Machine Learning Models to Reflect Human Knowledge and Values

Spatial machine-learning model diagnostics: a model-agnostic distance-based approach

Local Interpretability of Calibrated Prediction Models: A Case of Type 2 Diabetes Mellitus Screening Test

Related Research

Interpretability of machine learning based prediction models in healthcare

ML Interpretability: Simple Isn't Easy

Monitoring machine learning (ML)-based risk prediction algorithms in the presence of confounding medical interventions

Understanding Interpretability by generalized distillation in Supervised Classification

Oversampling Higher-Performing Minorities During Machine Learning Model Training Reduces Adverse Impact Slightly but Also Reduces Model Accuracy

Interpretability, Then What? Editing Machine Learning Models to Reflect Human Knowledge and Values

Spatial machine-learning model diagnostics: a model-agnostic distance-based approach