{'fraction_non_alphanumeric': 0.016569200779727095, 'fraction_numerical': 0.0, 'mean_word_length': 5.005847953216374, '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}}
Analyzing the Association between Stroke and Chronic Diseases

Stroke is a neurological disease that occurs when brain cells die due to lack of oxygen and nutrition. If a stroke is detected within a few hours, it can increase the chance of preventing complications. The main causes of death for modern people revolve around various chronic diseases like heart disease, hypertension and stroke. In this paper, we use the data of chronic diseases in the United States to analyze the situation of stroke caused by chronic diseases. The analysis of the basic patient data may be used to predict the high risk of stroke. This work focuses on using different data mining techniques to make different prediction modules. Four data mining techniques, namely, Random Forest, K-Nearest Neighbors, Logistics Regression and Decision Tree, are used to make predictions and the results are compared with each other using various evaluation matrix. This analysis methodology is suitable for examining data mining algorithms with health data.