Accurate prediction of no-show patients plays a crucial role as it enables researchers to increase the efficiency of their scheduling systems. The purpose of the current study is to formulate a novel hybrid data mining-based methodology to a) accurately predict the no-show patients, b) build a parsimonious model by employing a comprehensive variable selection procedure, c) build a model that does not suffer due to data imbalance, and d) provide healthcare agencies with a patient-specific risk level. Our study suggests that an Artificial Neural Network (ANN) model should be employed as a classification algorithm in predicting patient no-shows by using the variable set that is commonly selected by a Genetic Algorithm (GA) and Simulated Annealing (SA). In addition, we used Random Under Sampling (RUS) to improve the performance of the model in predicting the minority group (no-show) patients. The patient-specific risk scores were justified by applying a threshold sensitivity analysis. Also, the web-based decision support tool that can be adopted by clinics is developed. The clinics can incorporate their own intuition/incentive to make the final decision on the cases where the model is not confident enough (i.e. when the estimated probabilities fall near the decision boundary). These insights enable health care professionals to improve clinic utilization and patient outcomes.

准确预测未出现患者的病情起着至关重要的作用，因为它使研究人员能够提高其日程安排系统的效率。当前研究的目的是制定一种新颖的基于混合数据挖掘的方法，以：a）准确预测未出现患者； b）通过采用全面的变量选择程序来建立简约模型； c）不能建立模型。由于数据不平衡而遭受苦难，并且d）为医疗机构提供特定于患者的风险水平。我们的研究表明，应使用人工神经网络（ANN）模型作为分类算法，通过使用遗传算法（GA）和模拟退火通常选择的变量集来预测患者的缺席情况。（SA）。此外，我们使用随机抽样不足（RUS）来提高模型在预测少数群体（未出现）患者时的性能。通过应用阈值敏感性分析来证明患者特定的风险评分是合理的。此外，还开发了可被诊所采用的基于Web的决策支持工具。诊所可以结合自己的直觉/动机来对模型没有足够信心的情况（即，当估计的概率接近决策边界时）做出最终决定。这些见解使医疗保健专业人员能够提高临床利用率和患者预后。