Disease Prediction using Neural Network Test phase, srs, design phase and source code final deliverable

Disease Prediction using Neural Network Test phase, srs, design phase and source code final deliverable

Category

Artificial Intelligence + Web Based Application

Abstract / Introduction

This system will help users as well as doctors to diagnose the disease by automated symptoms detection system at earlier stages. There are 42 different disease data in given Disease Symptom Prediction data set. A detailed symptoms data is given which will be used to train the model for prediction.

Functional Requirements:

Follow the given life cycle to develop this system.

https://www.javatpoint.com/machine-learning-life-cycle

There are 2 modules and both of them are mandatory to develop this system.

Module 1:

• Import Data : Import the dateset files in system.(first download data set from https://www.kaggle.com/datasets/itachi9604/disease-symptom-description-dataset)
• Display Data: Display the summary statistics, trends, patterns and insights on the data visually by performing the EDA (Exploratory Data Analysis).
• Pre-process the data o Split the data into train (70% of given data set) and test(30% of given data set).
  • Train the model using Neural Network (machine learning algorithm).
• Testing: Apply test data on trained model for evaluation.
• Results: Generate a confusion matrix to assess the Accuracy, Precision, Recall, F1 score in the trained model.
• Save the model for future use.

Module 2:

• Build an interface using any GUI Python Library or a Web page using python web framework Django or Flask.
• System will have the register and login interfaces to get access of system. Also user’s detail and session tracking must be saved in database.
• The interface should provide the user an option to interact with the system, by first entering his/her symptoms, and predicting which disease is maximum related to symptoms.
• System must generate and display a response as following and save in database against each user’s details.
  • User Name: ……………………… o Disease Name: ……………………….
  • Symptoms: …………………… o Precautions: …………………

Pre-requisite:

Note: In order to completely understand the machine learning and artificial intelligence algorithms, watch given tutorials and also google for better understanding.

• https://www.javatpoint.com/machine-learning
• https://ocw.vu.edu.pk/Videos.aspx?cat=Computer+Science%2fInformation+Technol ogy+&course=CS607
• https://www.youtube.com/watch?v=_u-PaJCpwiU&list=PLu0W_9lII9ai6fAMHpacBmJONT7Y4BSG&index=1
• https://vulms.vu.edu.pk/Courses/CS607/Downloads/AI_Complete_handouts_for_Pr pdf DataSet:

https://www.kaggle.com/datasets/itachi9604/disease-symptom-description-dataset Python Tutorials:

• https://www.programiz.com/python-programming
• https://www.tutorialspoint.com/python/index.htm
• https://www.w3schools.com/python/ EDA Tutorials:
• https://www.analyticsvidhya.com/blog/2021/05/exploratory-data-analysis-eda-a-step-bystep guide/#:~:text=EDA%20is%20the%20process%20of,to%20understand%20the%20data%20b etter.
• https://www.geeksforgeeks.org/what-is-exploratory-data-analysis/ Machine Learning Tutorials:
• https://machinelearningmastery.com/machine-learning-in-python-step-by-step/
• https://www.geeksforgeeks.org/machine-learning-with-python/
• https://www.youtube.com/watch?v=ZftI2fEz0Fw
• https://www.simplilearn.com/10-algorithms-machine-learning-engineers-need-to-knowarticle
• https://www.analyticsvidhya.com/blog/2017/09/common-machine-learning-algorithms/ Deep learning Tutorials:
• https://www.tutorialspoint.com/python_deep_learning/index.htm
• https://deeplizard.com/learn/video/gZmobeGL0Yg
• https://www.simplilearn.com/tutorials/deep-learning-tutorial/deep-learning-algorithm
• https://www.javatpoint.com/deep-learning-algorithms Tkinter:
• https://www.tutorialspoint.com/python/python_gui_programming.htm
• https://www.geeksforgeeks.org/python-gui-tkinter/ Django:
• https://www.djangoproject.com/start/ https://www.w3schools.com/django/ Flask:
• https://www.javatpoint.com/flask-tutorial

Tools:

Language: Python (Only python language)

Framework: Anaconda, Tkinter, PyQt5, Django,Flask, etc. IDE: Jupyter Notebook, Spyder, Visual Studio Code, etc.

Supervisor:

Name: Neelam Alam