TY -的A2 Asghar穆罕默德Zubair盟,库雷希Zeeshan盟——Maqbool阿伊莎盟——殿下,艾琳娜AU -伊克巴尔,穆罕默德Zubair AU -阿夫扎尔,Farkhanda盟——Kanubala Deborah Dormah AU - Rana,总会有非盟- Umair米尔亚希尔AU -瓦克尔,Abdul AU -沙阿说,哈立德PY - 2021 DA - 2021/10/22 TI -有效地预测了临床使用机器学习SP - 2376391六世任命- 2021 AB -公共卫生及其相关设施蓬勃发展的城市和社会的关键。卫生资源的最佳利用节省金钱和时间,但最重要的是,它节省了宝贵的生命。在现在已经变得更加明显随着大流行过度现有的医疗资源。特定的病人预约调度,失踪的休闲态度医疗预约(no-show-ups)可能引起严重损害患者的健康。本文借助机器学习,我们分析六百万+病人预约记录来预测病人的行为/特点使用十个不同的机器学习算法。为此,我们首先从原始数据中提取有意义的特性使用数据清洗。我们应用合成少数过采样技术(杀),自适应合成抽样法(Adasyn)和随机采样(俄文)来平衡我们的数据。平衡后,我们应用十个不同的机器学习算法,即随机森林分类器、决策树、逻辑回归,XG提振,梯度推进,演算法分类器,朴素贝叶斯、随机梯度下降法,多层感知器,支持向量机。我们分析这些结果的帮助下六个不同的指标,即。、回忆、准确性、精密、F1-score曲线下的面积和均方误差。 Our study has achieved 94% recall, 86% accuracy, 83% precision, 87% F1-score, 92% area under the curve, and 0.106 minimum mean square error. Effectiveness of presented data cleaning and feature selection is confirmed by better results in all training algorithms. Notably, recall is greater than 75%, accuracy is greater than 73%, F1-score is more significant than 75%, MSE is lesser than 0.26, and AUC is greater than 74%. The research shows that instead of individual features, combining different features helps make better predictions of a patient’s appointment status. SN - 1748-670X UR - https://doi.org/10.1155/2021/2376391 DO - 10.1155/2021/2376391 JF - Computational and Mathematical Methods in Medicine PB - Hindawi KW - ER -