Chronic patients are adapting to an emerging healthcare system assisted by the wireless body area network (WBAN). Medical data are not normal at all times; hence, the preference for critical data is expected to be high, and the requirement of security has become essential. This paper addresses a novel healthcare monitoring system (HMS) that adopts the IEEE 802.15.6 standard for WBAN. A modified IEEE 802.15.6 MAC is designed for predicting data types and residual energy on body sensors. An enhanced MAC-based HMS (E-MHMS) is developed for delay aware data transmission to perform data aggregation, key distribution, channel selection and data classification. A smartphone acts as a coordinator that aggregates data from the WBAN; upon receiving the data, it determines the best channel from the available multiple inputs for assured data transmission. E-MHMS uses the novel time-based elliptic curve algorithm and the ASCII RSA algorithm for key distribution and encryption. Finally, the data reaches the monitoring servers that classify the data using hybrid naïve Bayesian neural network. The proposed E-MHMS setup in an OMNeT++ simulation environment and the improvements are demonstrated in terms of important network parameters such as delay, throughput, packet drop, security and accuracy.