{'fraction_non_alphanumeric': 0.016600790513833993, 'fraction_numerical': 0.0, 'mean_word_length': 4.888372093023256, '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}}
Early diagnosis of dengue disease using fuzzy inference system

Fuzzy expert system is a knowledge-based system, which is considered as one of the most common form of artificial intelligence in medicine(AIM) system with medical knowledge of a particularly defined task, and able to reach a proper conclusion by using the specific data from individual patient. In fuzzy inference system, a set of rules are used for representing the knowledge or data of a particular problem. Dengue fever, caused by the dengue virus, a mosquito-borne human viral pathogen is an infectious tropical disease. In a small proportion of cases Dengue disease is considered as one of the life threatening disease and delay of the diagnosis may lead to increase the risk level of the disease. Therefore, it is very important to detect the dengue disease at early stage. Thus this work was aimed to design an expert system for the early diagnosis of dengue disease using Fuzzy Inference System (FIS), a powerful tool for dealing with imprecision and uncertainty. The designed FIS can be used for early diagnosis of dengue disease of a patient by using his/her physical symptoms and medical test reports as input variables and converting these input variables into fuzzy membership functions.