Predicting mortality in upper gastrointestinal tract cancer using long short-term memory neural networks.

Abstract

340 Background: Upper gastrointestinal (GI) tract cancers represent a significant global health burden, characterized by high morbidity and mortality rates, with an increasing incidence among aging populations and varying outcomes based on gender and ethnicity. Early prediction of mortality rates in upper gastrointestinal cancer can significantly enhance treatment strategies and patient outcomes. This study describes current trends in upper gastrointestinal cancer and proposes a predictive model, developed using Long Short-Term Memory (LSTM) neural networks to project future trends using demographic data. Methods: We extracted mortality data from the Center for Disease Control and Prevention (CDC) Wide-Ranging Online Data for Epidemiological Research (WONDER) Database spanning 1999, normalized it and encoded categorical variables such as age, sex, ethnicity, and race numerically. Using Studio R to analyze mortality trends, we developed an LSTM model known for its ability to capture and utilize long term data patterns. The model, trained with a sequence length of 20, predicted future data points based on the previous 20. We then assessed its accuracy and generalization with a test set. Ultimately, our model is able to utilize demographic variables to forecast annual mortality and survival rates. Results: The mortality rates for upper GI tract cancers are notably higher among elderly individuals, particularly those aged 80 and above, with males and certain ethnic groups, such as Hispanics. The accuracy of the trained model was evaluated by calculating the Mean Squared Error (MSE), a measure of the difference between the predicted and actual value, with zero indicating no difference between the two. Our LSTM model achieved a training MSE of 0.00085 and a validation MSE of 0.0016, indicating good reliability and accuracy. Conclusions: Our analysis highlighted clear mortality trends and demonstrated the model’s effectiveness in improving mortality predictions in upper gastrointestinal cancer. However, limitations due to the dataset’s lack of clinical variables (such as comorbidities, cancer stage, and prior treatments) and its reliance on a single dataset may affect external validity. Future research is needed to determine if the model can be applied for clinical prognosis or strategic planning. We ultimately created and validated an LSTM neural network model with the CDC WONDER dataset, showing promising results. Future development will aim to include clinical characteristics to create a more personalized predictive tool.