Heath-care chatbot

Dev
Project Overview
This project aimed to create an intelligent chatbot capable of diagnosing common illnesses based on user-reported symptoms. Leveraging advancements in artificial intelligence, natural language processing (NLP), and machine learning, the chatbot provides medical diagnoses with a 75% accuracy rate. The motivation behind this project was to offer a tool that could assist users in identifying potential health conditions, particularly in areas with limited access to healthcare professionals.
My Contributions
I developed the entire chatbot framework using Python and integrated it with NLP algorithms and machine learning models to ensure accurate symptom analysis. My role involved:
Implementing the NLP pipeline to process user input and map it to relevant medical terms.
Training the machine learning model to classify symptoms into potential diagnoses, achieving 75% accuracy.
‍Testing and refining the model by feeding it real-world symptom data to improve its diagnostic performance.
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Designing the chatbot’s user interface, making it simple and intuitive for non-technical users.Through this project, I enhanced my skills in AI, machine learning, and NLP, applying them to solve real-world healthcare challenges.
The healthcare chatbot developed in this project leverages machine learning algorithms and a graphical user interface (GUI) to provide users with disease predictions based on the symptoms they input. The chatbot’s primary goal is to offer users a quick and reliable method for identifying potential health conditions, particularly in scenarios where access to healthcare professionals is limited.
The core functionality of the chatbot is powered by decision tree and support vector machine (SVM) classifiers, which analyze the entered symptoms and predict possible illnesses. The chatbot is designed to handle a variety of medical conditions, using preprocessing techniques to clean and structure the input data for more accurate predictions.
One of the unique aspects of the chatbot is its comprehensive symptom management. Beyond just offering a diagnosis, the chatbot provides users with detailed descriptions of their symptoms, the severity of the predicted disease, and recommended precautions. This additional layer of information makes the tool more valuable for users seeking to understand the implications of their symptoms and take preventive measures.
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‍Graphical User Interface (GUI)
The chatbot's GUI was built using the Tkinter library in Python, ensuring a user-friendly and intuitive interface. The GUI allows users to:Input multiple symptoms through a simple form.Receive a list of predicted diseases based on their input.View descriptions, severities, and recommended precautions for each predicted disease.The chatbot also includes text-to-speech functionality via pyttsx3, which enhances its accessibility for users with visual impairments. This feature reads out the diagnosis, making the tool more inclusive for individuals who may prefer audio responses or have difficulties reading the output.
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This project not only improved my proficiency in machine learning and GUI development but also allowed me to apply technology in solving practical healthcare challenges, ensuring the tool could be easily used by a wide audience.
Chatbot Creation
College Project
Jan2024 —Apr 2024
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