Volume 1 · Issue 1 (2024)
Hybrid Deep Learning Framework for Genetic Risk Prediction
Siqi Bo 1, Jiaojiao Zhang 1, Xiangyu Long 2
1 City Institute, Dalian University of Technology, Dalian, China
2 Guilin Tourism University, Guilin, China
The paper is a project of the 2024 Innovation and Entrepreneurship Training Program for Undergraduates of City Institute, Dalian University of Technology (Item No. X202413198007).
Abstract: Genetic risk prediction plays a crucial role in personalized healthcare by identifying high-risk individuals and guiding early interventions. The paper introduces a hybrid framework combining advanced deep learning architectures with traditional machine learning models to address the challenges of high-dimensional genomic data. By leveraging feature importance analysis, interaction modeling, and time-series techniques, the proposed model achieves robust predictions, outperforming existing methods with an accuracy of 89% and an AUC of 0.92. The framework identifies key contributors, such as pollution indices and environmental factors, through a grey comprehensive evaluation method. This scalable and interpretable approach holds significant potential for improving clinical decision-making and public health strategies.
Keywords: Genetic Risk Prediction; Hybrid model; Feature engineering; Machine learning
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