Summary of Predictive Modeling For Breast Cancer Classification in the Context Of Bangladeshi Patients: a Supervised Machine Learning Approach with Explainable Ai, by Taminul Islam et al.
Predictive Modeling for Breast Cancer Classification in the Context of Bangladeshi Patients: A Supervised Machine Learning Approach with Explainable AI
by Taminul Islam, Md. Alif Sheakh, Mst. Sazia Tahosin, Most. Hasna Hena, Shopnil Akash, Yousef A. Bin Jardan, Gezahign Fentahun Wondmie, Hiba-Allah Nafidi, Mohammed Bourhia
First submitted to arxiv on: 6 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a machine learning-based approach to diagnose breast cancer more efficiently. It compares the classification accuracy of five different machine learning methods – decision tree, random forest, logistic regression, naive bayes, and XGBoost – using a primary dataset from Dhaka Medical College Hospital. The study found that XGBoost achieved the best model accuracy with a score of 97%. Additionally, it applied SHAP analysis to the XGBoost model to interpret its predictions and understand the impact of each feature on the output. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Breast cancer is a growing concern worldwide. Doctors need help diagnosing this illness quickly and accurately. This study uses machine learning and AI to improve breast cancer diagnosis. It compares five different methods to see which one works best. The study found that one method, called XGBoost, was the most accurate. It also used special tools to understand how the model made its predictions. This could help doctors trust the results more. |
Keywords
* Artificial intelligence * Classification * Decision tree * Logistic regression * Machine learning * Naive bayes * Random forest * Xgboost
