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One of the major challenges in wireless sensor network (WSN) research is to curb down congestion in the network's traffic, without compromising with the energy of the sensor nodes. Congestion affects the continuous flow of data, loss of information, delay in the arrival of data to the destination and unwanted consumption of significant amount of the very limited amount of energy in the nodes. Obviously, in healthcare WSN applications, particularly in the ones that cater to medical emergencies or in the ones that closely monitor critically ailing patients, it is desirable in the first place to avoid congestion from occurring and even if it occurs, to reduce the loss of data due to congestion. In this work, we address the problem of congestion in the nodes of healthcare WSN using a learning automata (LA)-based approach. Our primary objective in using this approach is to adaptively make the processing rate (data packet arrival rate) in the nodes equal to the transmitting rate (packet service rate), so that the occurrence of congestion in the nodes is seamlessly avoided. We maintain that the proposed algorithm, named as learning automata-based congestion avoidance algorithm in sensor networks (LACAS), can counter the congestion problem in healthcare WSNs effectively. An important feature of LACAS is that it intelligently' learns' from the past and improves its performance significantly as time progresses. Our proposed LA based model was evaluated using simulations representing healthcare WSNs. The results obtained through the experiments with respect to performance criteria having important implications in the healthcare domain, for example, the number of collisions, the energy consumption at the nodes, the network throughput, the number of unicast packets delivered, the number of packets delivered to each node, the signals received and forwarded to the medium access control (MAC) layer, and the change in energy consumption with variation in transmission range, have - shown that the proposed algorithm is capable of successfully avoiding congestion in typical healthcare WSNs requiring a reliable congestion control mechanism.