In clinical research, adequate use of gathered information to provide an intelligent framework to assist the doctors is a great challenge for the current biomedical research community. This study proposed a probabilistic deep neural network (PDNN) to select wart treatment method, where the layered structure of artificial neurons plays a crucial role in generating the optimal feature space. However, the probabilistic and thresholding technique is used to minimize the false negative and false positive instances. In the existing approaches, prediction accuracy and biasedness are major concerns in identifying the best wart treatment method. The benchmark dataset consists of 180 patients toward the selection of immunotherapy and cryotherapy treatment methods. Based on the feature descriptors about the wart, the baseline classifiers such as Naïve Bayes (NB), logistic regression and ensemble (LR), support vector machine (SVM), decision tree (DT), bagging, random forest (RF), and eXtreme Gradient Boosting (XGB) along with the developed PDNN was constructed by taking splitting ratio criteria into account. The standard statistical measures such as the measure of accuracy (MoA), error rate, sensitivity, specificity, and area under the curve (AUC) were considered to evaluate the predictive behavior. The proposed PDNN approach obtained promising results: moA, error rate, sensitivity, specificity, and measure of AUC as 0.9778, 0.0222, 0.9762, 0.9792, and 0.9818 while selecting immunotherapy and 0.9889, 0.0111, 1.0000, 0.9796, and 0.9970 in case of cryotherapy. The developed PDNN outperforms baseline classifiers and existing state-of-the-art wart treatment expert systems. The proposed model will improve the success rate and saves the diagnosing time. PDNN-based wart treatment identification system can be implemented in real time after consulting with a domain specialist.

在临床研究中，充分利用收集到的信息来提供智能框架来辅助医生是当前生物医学研究界的一大挑战。本研究提出了一种概率深度神经网络（PDNN）来选择疣治疗方法，其中人工神经元的分层结构在生成最佳特征空间方面起着至关重要的作用。然而，概率和阈值技术用于最小化假阴性和假阳性实例。在现有方法中，预测准确性和偏差是确定最佳疣治疗方法的主要问题。基准数据集由 180 名患者组成，用于选择免疫疗法和冷冻疗法治疗方法。基于关于疣的特征描述符，基线分类器，如朴素贝叶斯 (NB)、逻辑回归和集成 (LR)、支持向量机 (SVM)、决策树 (DT)、装袋、随机森林 (RF) 和极限梯度提升 (XGB) 以及开发的 PDNN 是通过考虑分光比标准构建的。标准统计量度，例如准确度 (MoA)、错误率、灵敏度、特异性和曲线下面积 (AUC) 被认为是评估预测行为。所提出的 PDNN 方法获得了有希望的结果：moA、错误率、灵敏度、特异性和 AUC 的测量值为 0.9778、0.0222、0.9762、0.9792 和 0.9818，同时选择免疫疗法和 0.9889、0.0111、1.00900、909 和 70906 例的治疗。 . 开发的 PDNN 优于基线分类器和现有最先进的疣治疗专家系统。所提出的模型将提高成功率并节省诊断时间。基于PDNN的疣治疗识别系统可在咨询领域专家后实时实施。