Detection of risk factors of PCOS patients with Local Interpretable Model-agnostic Explanations (LIME) Method that an explainable artificial intelligence model

ÇİÇEK, İpek BALIKÇI; Küçükakçali, Zeynep; Yağın, Fatma Hilal

doi:10.52876/jcs.1004847

Cited by 4 publications

(4 citation statements)

References 26 publications

(22 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Mardaoui et al 13 provided an analysis based on LIME for interpreting text data. Çicȩk et al 14 provided a lucid account of application of LIME for detection of risk factors of PCOS patients. Jain et al 15 explained sentiment analysis results on social media texts through visualization.…”

Section: Previous Work On Explainablementioning

confidence: 99%

Interpretation of Drop Size Predictions from a Random Forest Model Using Local Interpretable Model-Agnostic Explanations (LIME) in a Rotating Disc Contactor

Prabhu

Sane

Dhadwal

et al. 2023

Ind. Eng. Chem. Res.

View full text Add to dashboard Cite

Drop size is a crucial parameter for the efficient design and operation of the rotating disc contactor (RDC) in liquid–liquid extraction. The current work focuses on providing local and global explanations for the prediction of the drop size in a rotating disc contactor (RDC). The Random Forest (RF) regression model is a robust machine learning algorithm that can accurately capture complex relationships in the data. However, the interpretability of the model is limited. In order to address the issue of interpretability of the developed RF model, in the current work, we employed Local Interpretable Model-Agnostic Explanations (LIME) of the predictions of the RF model. This provides both local and global views of the model and thereby helps one to gain insights into the factors influencing predictions. We have provided local explanations depicting the impact of different attributes on the prediction of the output for any given input example. We have also obtained global feature importance, providing the top subset of informative attributes. We have also developed local surrogate models incorporating second order attribute interactions. This has provided important information about the effect of interactions on the drop size prediction. By augmenting the random forest model with LIME, it is possible to develop a more accurate and interpretable model for estimating the drop size in RDCs, ultimately leading to improved performance and efficiency.

show abstract

Section: Previous Work On Explainablementioning

confidence: 99%

Interpretation of Drop Size Predictions from a Random Forest Model Using Local Interpretable Model-Agnostic Explanations (LIME) in a Rotating Disc Contactor

Prabhu

Sane

Dhadwal

et al. 2023

Ind. Eng. Chem. Res.

View full text Add to dashboard Cite

show abstract

“…[9][10][11] The applicability of classification models to diagnose disease is highly dependent on the ability to interpret and explain the applied models. 12 It is vital to explain model predictions, particularly in medical imaging applications. 13,14 In recent years techniques have been developed to explain the decisions made by artificial intelligence (AI) models known as Explainable-AI (XAI).…”

Section: Introductionmentioning

confidence: 99%

“…Contrary to the first method, a different approach where there is no backpropagation algorithm and not limited to any specific model type, namely Local interpretable Model-agnostic Explanation (LIME) was developed. [8][9][10][11][12]17 LIME is a well-known and frequently used method to ensure the interpretability and explainability of the AI-based models. 12 As LIME is basically designed to be model agnostic, it can be used for various machine learning methods.…”

Section: Introductionmentioning

confidence: 99%

“…[8][9][10][11][12]17 LIME is a well-known and frequently used method to ensure the interpretability and explainability of the AI-based models. 12 As LIME is basically designed to be model agnostic, it can be used for various machine learning methods. The model created by this method specifies which parts or characteristics of the input data are more significant, and hence it makes the results of the model more interpretable.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multi-scale local explanation approach for image analysis using model-agnostic Explainable Artificial Intelligence (XAI)

Hajiyan

Ebrahimi

2023

Medical Imaging 2023: Digital and Computational Pathology

View full text Add to dashboard Cite

The recent success of deep neural networks has generated remarkable growth in Artificial Intelligence (AI) research and has received much interest over the past few years. One of the main challenges for the broad adoption of deep learning-based models such as Convolutional Neural Networks (CNN) is the lack of understanding of their decisions. To address this issue, Explainable Artificial Intelligence (XAI) has been proposed to shift toward more transparent AI, resulting in the development of techniques to explain decisions by AI models. This paper aims to explore and develop a multi-scale scheme of LIME (Local Interpretable Model-Agnostic Explanations) applied to image classification to explain decisions made by CNN models through heatmaps of coarse to finer scales. More precisely, when LIME highlights large superpixels from a coarse scale, there may be smaller regions in the corresponding superpixel that influenced the model’s prediction at some finer scale. In the proposed multi-scale scheme, two weighting approaches, one based on Gaussian distribution and another parameter-free framework will be introduced to produce visual explanations observed from different scales. Promising results for multi-scale classification heatmaps of histopathology images are presented. More specifically, we investigated the proposed multi-scale approach on Camelyon16 dataset. The results show that the explanations are faithful to the underlying model, and the visualizations are reasonably interpretable.

show abstract

Investigation of Usability of Artificial Intelligence Semantic Video Processing Methods in Medicine

2022

View full text Add to dashboard Cite

Aim: In this study, by using appropriate video/image processing techniques and CNN architecture, it is aimed to develop user-friendly software for healthcare professionals with various methods such as detection, identification, classification and tracking of polyps contained in the endoscopic images. Material and Methods: The dataset consisted of 345 images in total. These images are images described and validated by medical doctors (experienced endoscopists) of several classes, consisting of hundreds of images for each class, such as anatomical milestones, pathological findings, or gastrointestinal procedures in the digestive tract. The images were obtained from the web address https://datasets.simula.no/kvasir, which is open source for research and educational purposes. CNN and Max-Margin object detection method (MMOD), one of the deep neural network architectures in the Dlib library, was used in the modeling phase. In the evaluation of model performance, precision, recall, F1-score, average precision (AP), mean average precision (mAP), optimal localization recall precision (oLRP), mean optimal LRP, (moLRP) and intersection over union (IoU) were used. Results: In the implementation of the study, when the previously described steps on the open access video image dataset related to the colonic polyps were performed, all performance metrics examined in the training dataset were 100%, while precision of 98%, recall of 94%, F1-score of 94%, AP of 89% and mAP of 89%, oLRP of 48% and moLRP of %48 were calculated on the testing dataset. Conclusion: Considering the values of the calculated performance criteria, it was found that the proposed system gave successful predictions in the diagnosis of gastrointestinal polyps.

show abstract

Detection of risk factors of PCOS patients with Local Interpretable Model-agnostic Explanations (LIME) Method that an explainable artificial intelligence model

Cited by 4 publications

References 26 publications

Interpretation of Drop Size Predictions from a Random Forest Model Using Local Interpretable Model-Agnostic Explanations (LIME) in a Rotating Disc Contactor

Interpretation of Drop Size Predictions from a Random Forest Model Using Local Interpretable Model-Agnostic Explanations (LIME) in a Rotating Disc Contactor

Multi-scale local explanation approach for image analysis using model-agnostic Explainable Artificial Intelligence (XAI)

Investigation of Usability of Artificial Intelligence Semantic Video Processing Methods in Medicine

Contact Info

Product

Resources

About