A Hybrid Machine Learning Model for Classifying Gene Mutations in Cancer using LSTM, BiLSTM, CNN, GRU, and GloVe
arxiv(2023)
摘要
In our study, we introduce a novel hybrid ensemble model that synergistically
combines LSTM, BiLSTM, CNN, GRU, and GloVe embeddings for the classification of
gene mutations in cancer. This model was rigorously tested using Kaggle's
Personalized Medicine: Redefining Cancer Treatment dataset, demonstrating
exceptional performance across all evaluation metrics. Notably, our approach
achieved a training accuracy of 80.6
an F1 score of 83.1
(MSE) of 2.596. These results surpass those of advanced transformer models and
their ensembles, showcasing our model's superior capability in handling the
complexities of gene mutation classification. The accuracy and efficiency of
gene mutation classification are paramount in the era of precision medicine,
where tailored treatment plans based on individual genetic profiles can
dramatically improve patient outcomes and save lives. Our model's remarkable
performance highlights its potential in enhancing the precision of cancer
diagnoses and treatments, thereby contributing significantly to the advancement
of personalized healthcare.
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