Challenges and Solutions of Machine Learning in Big Health Data Analytics: A Comprehensive Review

With the rapid growth of digital health records, wearable sensors, and biomedical imaging, an unprecedented quantity of heterogeneous and large-scale health data has emerged. Machine Learning (ML) and Deep Learning (DL) have shown exceptional potential in transforming healthcare by enabling accurate diagnosis, predictive modeling, and personalized treatment planning. However, integrating ML into clinical practice faces multiple challenges including data heterogeneity, bias, interpretability, ethical constraints, and computational burdens. This paper provides a comprehensive review across eight studies addressing these barriers and exploring emerging solutions such as federated learning, hybrid neural architectures (CNN–LSTM), data standardization, and explainable AI (XAI). The review highlights that successful ML-driven health analytics requires a synergistic balance between methodological innovation and ethical governance.

کلیدواژه ها:

Machine Learning ، Big Health Data ، Deep Learning ، Data Heterogeneity ، Explainable AI ، Federated Learning ، Biomedical Analytics ، Predictive Modeling