Summary of Prediction and Detection Of Terminal Diseases Using Internet Of Medical Things: a Review, by Akeem Temitope Otapo et al.
Prediction and Detection of Terminal Diseases Using Internet of Medical Things: A Review
by Akeem Temitope Otapo, Alice Othmani, Ghazaleh Khodabandelou, Zuheng Ming
First submitted to arxiv on: 22 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 integration of Artificial Intelligence (AI) and Internet of Medical Things (IoMT) in healthcare has advanced the prediction and diagnosis of chronic diseases through Machine Learning (ML) and Deep Learning (DL) techniques. AI-driven models like XGBoost, Random Forest, CNNs, and LSTM RNNs have achieved over 98% accuracy in predicting heart disease, chronic kidney disease, Alzheimer’s disease, and lung cancer using datasets from platforms like Kaggle, UCI, private institutions, and real-time IoMT sources. However, challenges persist due to variations in data quality, patient demographics, and formats from different hospitals and research sources. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AI models have struggled with overfitting, performing well in controlled environments but less effectively in real-world clinical settings. The incorporation of IoMT data adds complexities in ensuring interoperability and security to protect patient privacy. Future research should focus on data standardization and advanced preprocessing techniques to improve data quality and interoperability. Transfer learning and ensemble methods are crucial for improving model generalizability across clinical settings. |
Keywords
» Artificial intelligence » Deep learning » Lstm » Machine learning » Overfitting » Random forest » Transfer learning » Xgboost
