Dementia interferes with the individual’s motor, behavioural, and intellectual functions, causing him to be unable to perform instrumental activities of daily living. This study is aimed at identifying the best performing algorithm and the most relevant characteristics to categorise individuals with HIV/AIDS at high risk of dementia from the application of data mining. Principal component analysis (PCA) algorithm was used and tested comparatively between the following machine learning algorithms: logistic regression, decision tree, neural network, KNN, and random forest. The database used for this study was built from the data collection of 270 individuals infected with HIV/AIDS and followed up at the outpatient clinic of a reference hospital for infectious and parasitic diseases in the State of Ceará, Brazil, from January to April 2019. Also, the performance of the algorithms was analysed for the 104 characteristics available in the database; then, with the reduction of dimensionality, there was an improvement in the quality of the machine learning algorithms and identified that during the tests, even losing about 30% of the variation. Besides, when considering only 23 characteristics, the precision of the algorithms was 86% in random forest, 56% logistic regression, 68% decision tree, 60% KNN, and 59% neural network. The random forest algorithm proved to be more effective than the others, obtaining 84% precision and 86% accuracy.

中文翻译：

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数据挖掘算法在艾滋病患者痴呆症中的应用

痴呆症会干扰个人的运动、行为和智力功能，导致他无法进行日常生活的工具性活动。本研究旨在通过应用数据挖掘确定性能最佳的算法和最相关的特征，以对痴呆症高风险的 HIV/AIDS 患者进行分类。使用主成分分析（PCA）算法并在以下机器学习算法之间进行了比较测试：逻辑回归、决策树、神经网络、KNN和随机森林。本研究使用的数据库是根据 270 名 HIV/AIDS 感染者的数据收集建立的，并于 2019 年 1 月至 4 月在巴西塞阿拉州一家传染病和寄生虫病参考医院的门诊进行随访。还，针对数据库中可用的 104 个特征分析了算法的性能；然后，随着维数的减少，机器学习算法的质量得到了提高，并发现在测试过程中，甚至损失了大约 30% 的变化。此外，当仅考虑 23 个特征时，算法在随机森林中的精度为 86%，逻辑回归为 56%，决策树为 68%，KNN 为 60%，神经网络为 59%。随机森林算法被证明比其他算法更有效，获得了 84% 的准确率和 86% 的准确率。甚至失去了大约 30% 的变异。此外，当仅考虑 23 个特征时，算法在随机森林中的精度为 86%，逻辑回归为 56%，决策树为 68%，KNN 为 60%，神经网络为 59%。随机森林算法被证明比其他算法更有效，获得了 84% 的准确率和 86% 的准确率。甚至失去了大约 30% 的变异。此外，当仅考虑 23 个特征时，算法在随机森林中的精度为 86%，逻辑回归为 56%，决策树为 68%，KNN 为 60%，神经网络为 59%。随机森林算法被证明比其他算法更有效，获得了 84% 的准确率和 86% 的准确率。