Extraction of human understandable insight from machine learning model for diabetes prediction

Abstract

Explaining the reason for model’s output as diabetes positive or negative is crucial for diabetes diagnosis. Because, reasoning the predictive outcome of model helps to understand why the model predicted an instance into diabetes positive or negative class. In recent years, highest predictive accuracy and promising result is achieved with simple linear model to complex deep neural network. However, the use of complex model such as ensemble and deep learning have trade-off between accuracy and interpretability. In response to the problem of interpretability, different approaches have been proposed to explain the predictive outcome of complex model. However, the relationship between the proposed approaches and the preferred approach for diabetes prediction is not clear. To address this problem, the authors aimed to implement and compare existing model interpretation approaches, local interpretable model agnostic explanation (LIME), shapely additive explanation (SHAP) and permutation feature importance by employing extreme boosting (XGBoost). Experiment is conducted on diabetes dataset with the aim of investigating the most influencing feature on model output. Overall, experimental result evidently appears to reveal that blood glucose has the highest impact on model prediction outcome.

Short Review of Intention Mining in Social Crisis Management through Automatic Technologies

“…In different application domains, researchers have explored the interpretability of deep models. In the medical field, Assegie et al [14] adopted LIME and SHAP to rank the importance of features and explain the model's output on whether a patient is diabetic or not. To provide clinical doctors with a clear understanding of the classification criteria utilized by GNN in Alzheimer's disease prediction, Anjomshoae et al [15] proposed a single node classification explanation method.…”

Analysis of Interpersonal Relationships of Social Network Users Using Explainable Artificial Intelligence Methods

“…Lundberg developed the G-DeepShap method for explaining complex machine learning models and tested its comprehensive performance evaluation on biological, medical and financial datasets [35]. In [36], a study was conducted on individual parameters that affect the prediction of diabetes in patients. Authors used various combinations (LIME -local interpreted explanations and SHAP).…”

The Prediction of Diabetes

“…[51] use the Pedigree Diabetes Function variable together with others with the Ensemble Machine Learning Techniques. [52] consider the positive role of diabetes pedigree function and weight in determining diabetes with machine learning algorithms. [53] uses the Pedigree Function Diabetes variable along with other variables to predict diabetes using fuzzy methods.…”