Optimization of Adaboost cardiac disease prediction and classification based on long and short term memory network

In this paper, the optimization of Adaboost algorithm combined with LSTM (Long short-term memory network) model was carried out to predict heart disease, and the prediction effect of the model was verified by experiments. Specifically, after the training of the model, the loss value of the training set gradually decreases from the initial 0.67, and finally approaches 0 after 2500 epochs, showing that the prediction effect of the model is constantly improving. The accuracy of the training set also improved, gradually climbing from 40% at the beginning, to more than 90% after the 500th epoch, and finally stabilized at around 99%. This result shows that the model effectively learns the characteristics of the data and can predict heart disease with high accuracy. Further analysis of the confusion matrix of the training set showed that all the predictions of heart disease were correct with 100% accuracy. This fully shows that the model has excellent performance in learning and extracting data features, and has good classification ability. On the test set, after evaluation, the prediction accuracy of the model reaches 96.67%, which also reflects its excellent classification effect. This result further demonstrates the model's good generalization ability to maintain a high level of predictive performance on previously unseen data. This is of great practical significance for heart disease risk assessment in clinical practice. To sum up, the Adaboost algorithm optimization model based on LSTM proposed in this paper has achieved satisfactory results in the prediction of heart disease, demonstrating the potential and value of deep learning in the medical field. Future studies can further explore the improvement and refinement of the model to improve its application performance in other disease prediction.