A Supervised Learning Based Decision Support System for Multi-Sensor Healthcare Data from Wireless Body Sensor Networks
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A Supervised Learning Based Decision Support System for Multi‑Sensor Healthcare Data from Wireless Body Sensor Networks J. J. Jijesh1 · Shivashankar1 · Keshavamurthy1
© Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract 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%). Keywords Heart diseases · Predictions · Preprocessing · Segmentation · Feature extraction · Modified- deep belief network
1 Introduction The introduction of Wireless Body Area Networks (WBAN) has become an advantage to medical and healthcare domain servicing over a number of applications [1]. When the aging population is taken into consideration around the globe, there is a massive need for intelligent sensors which are able to measure the characteristic features of the human body based on the parameters required and transmit their data to the centralized controller [2]. * J. J. Jijesh [email protected] 1
Department of Electronics and Communication Engineering, Sri Venkateshwara College of Engineering, VTU, Bangalore, India
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The design of sensors is such that they are small manageable entities which consume less power for both transmitting and receiving data and are flexibly used across different networks [3]. For example, current advances in sensors and related hardware have empowered the improvement of bio-medical sensors that can be attached to the human body or implanted within [4]. These sensors have the ability to gather critical information about the variations in human body condition and in this way encouraging the presentation of new sorts of systems. This system has further enabled the patient under observation to move around freely without the need for him to visit the medical centers frequ
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