Prediction and Detection of Terminal Diseases Using Internet of Medical Things: A Review
<|reference_start|>Prediction and Detection of Terminal Diseases Using Internet of Medical Things: A Review: The integration of Artificial Intelligence (AI) and the Internet of Medical Things (IoMT) in healthcare, through Machine Learning (ML) and Deep Learning (DL) techniques, has advanced the prediction and diagnosis of chronic diseases. AI-driven models such as XGBoost, Random Forest, CNNs, and LSTM RNNs have achieved over 98\% accuracy in predicting heart disease, chronic kidney disease (CKD), 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. The incorporation of IoMT data, which is vast and heterogeneous, adds complexities in ensuring interoperability and security to protect patient privacy. AI models often struggle with overfitting, performing well in controlled environments but less effectively in real-world clinical settings. Moreover, multi-morbidity scenarios especially for rare diseases like dementia, stroke, and cancers remain insufficiently addressed. 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. Additionally, the exploration of disease interactions and the development of predictive models for chronic illness intersections is needed. Creating standardized frameworks and open-source tools for integrating federated learning, blockchain, and differential privacy into IoMT systems will also ensure robust data privacy and security.<|reference_end|>
@article{otapo2024prediction,
    title={Prediction and Detection of Terminal Diseases Using Internet of Medical
  Things: A Review},
    author={Akeem Temitope Otapo, Alice Othmani, Ghazaleh Khodabandelou, Zuheng
  Ming},
    journal={arXiv preprint arXiv:2410.00034},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.00034},
    primaryClass={cs.LG}
}