摘要
由机器人与机器学习每日新闻的新闻记者兼新闻编辑-研究人员详细介绍了人工智能的新数据。根据NewsR X编辑在Wollega大学的新闻报道，研究表明，"由人工智能驱动的自动疾病诊断和预测在使医疗专业人员向患者提供有效护理方面发挥了关键作用。"新闻记者从Wollega大学的研究中获得了一句话："虽然这种预测工具在资源丰富的语言中得到了广泛的探索，如英语。这篇手稿的重点是根据Afaan Oromo语言记录的症状自动预测疾病类别，采用Vario US分类算法。这项研究包括机器学习技术，如支持向量机、随机森林、逻辑回归和朴素贝叶斯，以及深度学习方法，包括LSTM、GRU和BI-LSTM。采用TF-IDF和Word Embedding两种特征表示方法,分别用准确率、召回率、精密度和F1评分对所提方法的性能进行了评价,实验结果表明,在机器学习模型中,采用TF-IDF的SVM模型准确率最高,F1评分为94.7%。而使用word2vec嵌入的LSTM模型对深度学习模型的准确率为95.7%，F1得分为96.0%。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Researchers detail new data in artific ial intelligence. According to news reporting out of Wollega University by NewsR x editors, research stated, “Automated disease diagnosis and prediction, powered by AI, play a crucial role in enabling medical professionals to deliver effecti ve care to patients.”The news reporters obtained a quote from the research from Wollega University: “ While such predictive tools have been extensively explored in resource-rich lang uages like English, this manuscript focuses on predicting disease categories aut omatically from symptoms documented in the Afaan Oromo language, employing vario us classification algorithms. This study encompasses machine learning techniques such as support vector machines, random forests, logistic regression, and Naive Bayes, as well as deep learning approaches including LSTM, GRU, and Bi-LSTM. Du e to the unavailability of a standard corpus, we prepared three data sets with d ifferent numbers of patient symptoms arranged into 10 categories. The two featur e representations, TF-IDF and word embedding, were employed. The performance of the proposed methodology has been evaluated using accuracy, recall, precision, a nd F1 score. The experimental results show that, among machine learning models, the SVM model using TF-IDF had the highest accuracy and F1 score of 94.7% , while the LSTM model using word2vec embedding showed an accuracy rate of 95.7% and F1 score of 96.0% from deep learning models.”
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