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@article{184612,
        author = {Nilesh Gupta and Manish Kumar Kushwaha},
        title = {Deep Learning Models for Biomarker Discovery and Disease Diagnosis from Blood Test Data},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {4},
        pages = {2101-2108},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=184612},
        abstract = {The use of data generated from blood tests in laboratories, epidemiology and public health investigations. A deep learning framework aimed at biomarker identification and disease diagnosis using hematological and biochemical test results is presented. The model includes blood cells, hemoglobin, hematocrit, platelets, glucose, cholesterol, electrolytes. The experiment showed that this deep learning model performed better than the machine learning method with an accuracy of 93.2%. An analysis indicated that the model could effectively detect complicated non-linear feature interactions. Through ablation tests, blood cell parameters were designated as clinically valuable predictors. Moreover, the model has identified important biomarkers that closely correlate with anemia, leukemia, diabetes, and CVDs, underscoring its clinical relevance. Strong evidence suggests that this model will allow for more accurate clinicopathological diagnoses and enable personalized risk stratification profiles. This study shows how deep learning can interpret blood data. This can significantly help precision medicine and medical research focused on biomarkers.},
        keywords = {Deep Learning, Biomarker Discovery, Blood Test Data, Disease Diagnosis, Clinical Decision Support.},
        month = {September},
        }