ArticleScientific reports2024
XAI-driven CatBoost multi-layer perceptron neural network for analyzing breast cancer.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Interpretable machine learning for prognostic prediction in young women with triple-negative invasive ductal breast cancer: a Boruta-SHAP integrated approach.Gland surgery · 2026Article
- HMC-net: a ResNet fused hierarchical multi-scale cross-attention architecture for mammographic breast malignancy recognition incorporating explainable AI.Frontiers in oncology · 2026Article
- Radiomics-based Machine Learning Models for Classifying Breast Cancer on Dynamic-Contrast Enhanced MRI through Multi-Observer AnalysisCurrent medical imaging · 2026Article
- HydroPredictor a hybrid machine learning model for addressing data scarcity in groundwater prediction.Scientific reports · 2025Article
- Software cost estimation using TabNet and Harris Hawks Optimization.Scientific reports · 2025Article
- Design and development of a portable multiwavelength LED-based diffuse reflectance spectroscopy tool for rapid breast cancer identification.Scientific reports · 2025Article
- Lectin Microarray-based Glycomics and Machine Learning Identify Shared Osteoarthritis Biomarkers in Humans, Dogs, and Horses.bioRxiv : the preprint server for biology · 2025Article
- Automated multi-model framework for malaria detection using deep learning and feature fusion.Scientific reports · 2025Article
- A tailored deep learning approach for early detection of oral cancer using a 19-layer CNN on clinical lip and tongue images.Scientific reports · 2025Article
- An explainable AI-driven deep neural network for accurate breast cancer detection from histopathological and ultrasound images.Scientific reports · 2025Article
- Exploring Agentic AI in Healthcare: A Study on Its Working Mechanism.Frontiers in medicine · 2025Article
- An interpretable machine learning model for predicting sepsis risk in ICU patients with non-traumatic subarachnoid hemorrhage: development and validation using the MIMIC-IV database.Frontiers in neurologyArticle
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Early diagnosis of breast cancer is exceptionally important in signifying the treatment results, of women's health. The present study outlines a novel approach for analyzing breast cancer data by using the CatBoost classification model with a multi-layer perceptron neural network (CatBoost+MLP). Explainable artificial intelligence techniques are used to cohere with the proposed CatBoost with the MLP model. The proposed model aims to enhance the interpretability of predictions in breast cancer diagnosis by leveraging the benefits of CatBoost classification technique in feature identification and also contributing towards the interpretability of the decision model. The proposed CatBoost+MLP has been evaluated using the Shapley additive explanations values to analyze the feature significance in decision-making. Initially, the feature engineering is done using the analysis of variance technique to identify the significant features. The MLP model alone and the CatBoost+MLP model are being analyzed using divergent performance metrics, and the results obtained are compared with contemporary breast cancer identification techniques.
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