Biomedical Data Analysis Models for Disease Identification

Zuoheng Anita Wang

Biomedical Data Analysis Models for Disease Identification

Author(s)	Zuoheng Anita Wang
Country	United States
Abstract	Biomedical data analysis has become an important component of modern healthcare research because the increasing availability of clinical, genomic, molecular, imaging, laboratory, and physiological information provides new opportunities for identifying diseases at earlier stages. Advanced analytical models can examine complex biomedical datasets, identify relevant patterns, and support disease classification, risk assessment, and clinical decision-making. This study examines biomedical data analysis models for disease identification, focusing on statistical learning, machine learning, deep learning, classification, clustering, feature extraction, and multimodal data integration. A qualitative descriptive and conceptual research design was adopted using secondary academic literature. The analysis indicates that biomedical data analysis models can support disease identification by processing heterogeneous datasets and extracting clinically meaningful information. Machine-learning approaches can identify relationships between biomedical characteristics and disease outcomes, while deep-learning models can analyze high-dimensional imaging, genomic, and molecular information. Effective disease identification depends on data quality, feature selection, model validation, interpretability, and clinical integration. However, challenges involving incomplete datasets, dimensionality, algorithmic bias, privacy, cybersecurity, interoperability, model generalizability, and clinical validation remain significant. The study concludes that biomedical data analysis models can strengthen disease identification when developed using representative datasets, rigorous validation, appropriate analytical methods, and clinically relevant interpretation. Future research should emphasize multimodal biomedical data integration, explainable artificial intelligence, federated learning, personalized disease identification, real-time analytics, and robust clinical validation.
Keywords	Biomedical Data, Disease Identification, Machine Learning, Deep Learning, Biomedical Informatics, Data Analysis, Disease Classification, Predictive Analytics, Clinical Decision Support, Artificial Intelligence.
Field	Engineering
Published In	Volume 5, Issue 3, May-June 2023
Published On	2023-05-14

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American International Journal of Advanced Research and Development

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Biomedical Data Analysis Models for Disease Identification

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