{'fraction_non_alphanumeric': 0.033623910336239106, 'fraction_numerical': 0.0, 'mean_word_length': 5.330708661417323, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Predicting Stroke Disease Based on Recurrent Neural Network and Optimization techniques

Stroke disease is one of the most prevalent diseases all over the world. This paper presents a powerful early stroke prediction system that uses medical records that describe whether a person is infected or not. We proposed an optimized DeepRNN based on different layers of A Recurrent Neural Network (RNN) and KerasTuner optimization technique for predication stroke disease. The proposed model is compared with other ML models: Decision Tree (DT), Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), K Nearest Neighbors (K-NN), and Naive Bayes (NB). The GridsearchCV technique optimized ML models. The results showed that DeepRNN was the highest performance model compared with ML models.