Wireless body sensor network (WBSN) is also known as wearable sensors with transmission capabilities, computation, storage and sensing. In this paper, a supervised learning based decision support system for multi sensor (MS) healthcare data from wireless body sensor networks (WBSN) is proposed. Here, data fusion ensemble scheme is developed along with medical data which is obtained from body sensor networks. Ensemble classifier is taken the fusion data as an input for heart disease prediction. Feature selection is done by the squirrel search algorithm which is used to remove the irrelevant features. From the sensor activity data, we utilized the modified deep belief network (M-DBN) for the prediction of heart diseases. This work is implemented by Python platform and the performance is carried out of both proposed and existing methods. Our proposed M-DBN technique is compared with various existing techniques such as Deep Belief Network, Artificial Neural Network and Conventional Neural Network. The performance of accuracy, recall, precision, F1 score, false positive rate, false negative and true negative are taken for both proposed and existing methods. Our proposed performance values for accuracy (95%), precision (98%), and recall (90%), F1 score (93%), false positive (72%), false negative (98%) and true negative (98%).

无线人体传感器网络（WBSN）也被称为可穿戴传感器，具有传输功能，计算，存储和传感功能。本文提出了一种基于监督学习的决策支持系统，用于来自无线人体传感器网络（WBSN）的多传感器（MS）医疗保健数据。在此，与从身体传感器网络获得的医学数据一起，开发了数据融合集成方案。集合分类器将融合数据用作心脏病预测的输入。特征选择是通过松鼠搜索算法完成的，该算法用于删除不相关的特征。从传感器活动数据中，我们利用改进的深度信念网络（M-DBN）预测心脏病。这项工作是通过Python平台实现的，并且性能是通过建议的方法和现有方法来实现的。我们将我们提出的M-DBN技术与各种现有技术进行比较，例如深度信任网络，人工神经网络和常规神经网络。提出的和现有的方法均采用准确性，召回率，准确性，F1得分，假阳性率，假阴性和真阴性的性能。我们建议的性能值包括准确性（95％），准确性（98％）和召回率（90％），F1分数（93％），假阳性（72％），假阴性（98％）和真阴性（98％） ）。