{'fraction_non_alphanumeric': 0.021032504780114723, 'fraction_numerical': 0.006373486297004461, 'mean_word_length': 4.7299270072992705, '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}}
Intelligent Analysis of Some Factors Accompanying Hepatitis B

Background. It is evident that the B hepatitis disease is favored by several risk factors. Among the factors analyzed in this study, gender, diabetes, arterial hypertension, and body mass index. The age of the first infection is related to these variables. As the system is very complex, because other factors can have an effect and which are ignored, this study processes data using artificial intelligence techniques. Method. The study concerns 30 patients diagnosed at our service of the university hospital of Setif in Algeria. The study period runs from 2011 to 2020. The risk factors are considered imprecise and therefore fuzzy. A fuzzy inference system is applied in this study. The data is fuzzyfied and a rule base is established. Results. As the principles of fuzzy logic deal with the uncertain, this allowed us to take care of this imprecision and complexity. The established rule base maps the inputs, which are the risk factors, to hepatitis as the output variable. Conclusion. Several factors promote hepatitis B. The physiological system differs from one individual to another. Also, the weight of each factor is ignored. Given this complexity, the principles of fuzzy logic proposed are adequate. Once the system has been completed, it allows the random introduction of values at the input to automatically read the result at the output. This tool can be considered as a prevention system in the appearance and and establish a typical profile of people likely to be affected by hepatitis.