Abstract: Technologies like AI ,machine learning, data science are becoming upgraded. The advancement in available, portable, low cost handheld devices like cell phones and availability of network connection has resulted within the user’s mobility at an unprecedented level. We've studied different methodologies like Smart goal annotation, state phase annotation, collection process, agreement results also as annotation skills for achieving the health goals. The user has got to type their health regarding the query based on that assistant giving the acceptable answer. The facilities like report generation and scheduling assignment are provided. It'll increase the interaction between humans and machines with the assistance of different technologies, vast dialogue ,conversational knowledge based, public knowledge based. The system uses different algorithms for disease recognition, behavior abnormality detection, prediction etc. Experimental results show that: Compared with traditional methods,the proposed method is more accurate and faster and patients can get service anywhere and anytime.

Keywords: Conversational Interface, Machine Learning, NLP Algorithm, Patient health monitoring, SVM Algorithm.

PDF | DOI: 10.17148/IJARCCE.2023.124108

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