Summary of Ai-driven Predictive Analytics Approach For Early Prognosis Of Chronic Kidney Disease Using Ensemble Learning and Explainable Ai, by K M Tawsik Jawad et al.
AI-Driven Predictive Analytics Approach for Early Prognosis of Chronic Kidney Disease Using Ensemble Learning and Explainable AI
by K M Tawsik Jawad, Anusha Verma, Fathi Amsaad, Lamia Ashraf
First submitted to arxiv on: 10 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposed AI-driven predictive analytics approach uses ensemble learning and explainable AI to aid clinical practitioners in prescribing lifestyle modifications for individual patients with Chronic Kidney Disease (CKD). The goal is to visualize dominating features, feature scores, and values for early prognosis and detection of CKD. A dataset was collected on body vitals from individuals with CKD and healthy subjects to develop the proposed AI-driven solution accurately. Ensemble tree-based machine-learning models were applied to predict unseen cases of CKD, and findings were validated after lengthy consultations with nephrologists. The Random Forest model identified more significant features than XgBoost, while the XgBoost model achieved a higher score in interpretability metrics, including Fidelity (98%) and FII index. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary For chronic kidney disease, researchers developed an AI-driven predictive analytics approach to help doctors recommend lifestyle changes for patients. They used data from people with kidney disease and healthy individuals to train their models, which can predict new cases of the disease. The team worked closely with nephrologists to validate their findings. This research could lead to earlier diagnosis and better treatment of chronic kidney disease. |
Keywords
» Artificial intelligence » Machine learning » Random forest » Xgboost
