## 2 Related Works  
Modern-day health service delivery is becoming more patient-centric than ever before. This new norm is instigating medical institutions across the world to seek urgent solutions on how to prioritize patients' medicare outcomes because a greater percentage of their reputation and profitability now depends on it [17]. Meanwhile, meeting every care need of patients is currently almost impossible for human physicians, considering the bottlenecks of gross maldistribution and shortage of healthcare workforce in the real-world scenario. Therefore, the paradigm and demand for patient-centric healthcare nowadays is introducing a new wave of physical and emotional stress problems for doctors and other health workers [18], which can result in low efficiency in delivering essential healthcare interventions and services. On the basis of the above-stated problem, a plethora of diverse research studies has explored the use of text-based chatbots as a solution to support doctors and patients towards effective healthcare service delivery in various areas of medicine. To this regard, scholars such as [PERSON] et al. [19] developed an SMS-based NLP technique engineered with a knowledge base in the domain of known disease facts to assist medical experts in successfully boosting medical service delivery. Additionally, other similar studies have widely covered the aspects of using chatbots to support healthy lifestyles [9], mental health [11], pregnancy care [20; 21], etc. [22]; however, very few related works have been conducted on the subject of oncology. With respect to breast cancer, [PERSON] et al. [14] developed an informative chatbot, referred to as Vik, which provides support to medically confirmed FBC patients and is capable of diagnosing other diseases as well. A subsequent study by [PERSON] et al. [23] investigated the effectiveness of the Vik chatbot from the patient-centric point of view and discovered that the use and acceptance of chatbots in healthcare can also improve patients' medication adherence. Judging from the perspective of [PERSON] et al. [22], the integral generic features of text-based chatbots such as being gender neutral, racially unbiased, cost-effective, and available round-the-clock to ask or respond coherently to chats when needed are among the appealing qualities that makes chatbots suitable in supporting patients' care needs. Recently, modelling of chatbots with deep neural network (DNN) algorithms has become the in-vogue approach widely used for the purpose of achieving intelligent dialog outcomes. Meanwhile, [PERSON] et al. [24] and [PERSON] et al. [21] considered this approach as being very data-intensive. The former [24] indicated that the output accuracy of some of the models based on DNN approach depends on the hugeness of the data size used for model training, which, as a result, often necessitates demand for extensive computational resources that can only be afforded by a limited audience. In confirmation, [PERSON] and [PERSON] [25] stated that DNNs trained with small dataset often exhibit unstable behavior in terms of their output performance. Considering that data availability for research in the closed domain of healthcare sector are often either scarce [14; 25; 26] due to strict medical ethics enacted to protect the privacy of patients' information or that the available data are unusable [27] poses some concerns regarding the application of DNNs in this regard. Although authors such as [PERSON] [28] have conducted experiments on DNN-based chatbot (e.g., stacked bidirectional long short-term memory (BiLSTM)) and achieved interesting results after training with small-scale dataset for a closed-domain task, such approaches may not yet be fully accredited for implementation in critical service domains such as the healthcare sector, where dialog errors can result to fatal implications [12; 14].