1.1 Introduction In today's digital landscape, people have access to an unprecedented amount of health information, which can be overwhelming to navigate. To support users in finding the health topics that matter most to them, we propose the development of a HealthConnect application designed to organize health articles and resources into predefined categories based on their subject matter. This initiative aims to systematically manage and retrieve content, making it more accessible and relevant for users seeking health-related information.
Utilizing advancements in machine learning and natural language processing (NLP), this project will develop robust models capable of automatically categorizing health articles based on their content. The outcome will lead to improved content organization, enhanced operational efficiency, and an elevated user experience—critical factors for any health-focused platform. The models developed will eventually be integrated into an application for healthcare providers and individuals, facilitating efficient information retrieval.
Aim The central aim of this project is to develop and deploy an automated health article classification system utilizing machine learning techniques. This system will be designed to accurately categorize articles into predefined categories, thereby enhancing content organization, improving operational efficiency, and increasing user satisfaction for the HealthConnect application.
1.2 Objectives Accurately Classify Articles: Create a model that can accurately categorize health articles into predefined categories, such as Nutrition, Fitness, Mental Health, Medical Conditions, and Wellness.
Improve Content Organization: Streamline the management and retrieval process of health-related content, making it more accessible and relevant to users.
Enhance User Satisfaction: Provide a better experience by enabling users to quickly and easily find health information that aligns with their interests and needs.
Increase Operational Efficiency: Automate the categorization process to reduce the workload on healthcare content curators and providers, allowing them to concentrate on delivering valuable information.
Deploy as an App: Transform the model into an intuitive HealthConnect application that can be used by healthcare providers and individuals to benefit from automated article classification and health information retrieval.
A simple Streamlit app
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Install the requirements
$ pip install -r requirements.txt -
Run the app
$ streamlit run streamlit_app.py
1.3 Packages The packages used in this project are the following: matplotlib, seaborn, python, numpy & pandas.
1.4 Team Members
Siphosethu Rululu
https://github.com/SIPHOSETHU2303
Keryn Beth Rabe
https://github.com/KerynBethRabe