Prevention in Healthcare: An Explainable AI Approach

Makubhai, Shahin; Pathak, Ganesh R; Chandre, Pankaj R

doi:10.17762/ijritcc.v11i5.6582

Cited by 6 publications

(2 citation statements)

References 19 publications

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“…It's essential to understand that XAI-based lung cancer risk prediction does not replace professional medical advice or diagnosis [58]. However, it can be useful in identifying people who might have an increased susceptibility to lung cancer development and who might profit from early detection and treatment.…”

Section: Resultsmentioning

confidence: 99%

Predicting lung cancer risk using explainable artificial intelligence

Shoukat Makubhai,

Pathak,

Chandre

2024

Bulletin EEI

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Lung cancer is a lethal disease that claims numerous lives annually, and early detection is essential for improving survival rates. Machine learning has shown promise in predicting lung cancer risk, but the lack of transparency and interpretability in black-box models impedes the understanding of factors that contribute to risk. Explainable artificial intelligence (XAI) can overcome this limitation by providing a clear and understandable approach to machine learning. In this study, we will use a large patient record dataset to train an XAI-based model that considers various patient information, including lifestyle factors, clinical data, and medical history, for predicting lung cancer risk. We will use different XAI techniques, including decision trees, partial dependence plots, and feature importance, to interpret the model’s predictions. These methods will provide healthcare professionals with a transparent and interpretable framework for screening and treatment decisions concerning lung cancer risk.

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Section: Resultsmentioning

confidence: 99%

Predicting lung cancer risk using explainable artificial intelligence

Shoukat Makubhai,

Pathak,

Chandre

2024

Bulletin EEI

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“…However, the small sample size of the dataset used in the study suggests that further research is needed to validate these results on larger datasets. Makubhai et al [20] aims to enhance lung cancer risk prediction using explainable AI techniques. By analyzing a diverse range of patient data, including lifestyle factors and medical history, the model offers transparent insights for healthcare professionals.…”

Section: Literature Surveymentioning

confidence: 99%

Comparative analysis of explainable AI models for predicting lung cancer using diverse datasets

Makubhai,

R Pathak,

R Chandre

2024

IJ-AI

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Lung cancer prediction is crucial for early detec-tion and treatment, and explainable AI models have gained attention for their interpretability. This study aims to compare various explainable AI models using diverse datasets for lung cancer prediction. Clinical, genomic, and imaging data from multiple sources were collected, prepro-cessed, and used to train models such as Logistic Regression, SVC-Linear, SVC-rbf, Decision Tree, Random Forest, AdaBoost Classifier, and XGBoost Classifier. Preliminary results indicate that Random Forest achieved the highest accuracy of 98.9% across multiple datasets. Evaluation metrics such as accuracy, precision, recall, and F1 score were utilized, along with interpretability techniques like feature importance rankings and rule extraction methods. The study's findings will aid in identifying effective and interpretable AI models, facilitating early detection and treatment decisions for lung cancer.

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