基于改进TF-IDF算法的牛疾病智能诊断系统

传统的TF-IDF(Term Frequency&Inverse Documentation Frequency)算法提取的关键词不能合理地代表某疾病的症状,降低智能诊断系统的性能。对此,提出一种改进的TF-IDF算法,并将其应用在牛疾病诊断系统中。系统将用户描述的文本内容转换成向量的形式,用TF-IDF算法提取关键症状词,利用余弦定理和可信度计算给出可靠的疾病推荐和治疗方案。实验结果表明,该算法在疾病诊断中准确率和可信度两方面都具有更好的效果。与传统TF-IDF算法相比,平均可信度提高约4%。

CATTLE DISEASE INTELLIGENT DIAGNOSIS SYSTEM BASED ON IMPROVED TF-IDF ALGORITHM

The of the keywords extracted by the traditional TF-IDF(Term Frequency&Inverse Documentation Frequency)algorithm can not reasonably represent the symptoms of disease,thus reducing the performance of intelligent diagnostic systems.In response to this situation,an improved TF-IDF algorithm is proposed and applied in the cattle disease diagnosis system.The system converted the text content described by the user into a vector form,extracted the key symptom words by TF-IDF algorithm,and used the cosine theorem and credibility calculation to give a reliable disease recommendations and treatment plans.The experimental results show that the algorithm has better effects in both disease accuracy and credibility.The average credibility is improved by about 4%compared with the traditional TF-IDF algorithm.