Patient disease prediction using machine learning

Project Title

Patient disease prediction using machine learning

Project Area of Specialization Artificial IntelligenceProject Summary

IT is crucial in supporting medical facilities and governments in their battle against the pandemic. The project goal is to create a unified system throughout the whole human species. We are designing a Disease Prediction system that uses different machine learning algorithms to predict diseases based on symptoms provided by patients or other users. Based on the symptoms, age, and gender of an individual, the diagnosis system gives the output as the disease that the individual might be suffering from. Our diagnosis model can act as a doctor for the early diagnosis of a disease to ensure the treatment can take place on time and lives can be saved. The above method is used to predict diseases using patient treatment history and health data.

Project Objectives

This project aims to develop a single platform that allows customers to predict their disease in just few clicks. People The prediction engine requires a large dataset and efficient machine learning algorithms to predict the presence of the disease. By using machine learning, a huge amount of data is required . So, the primary aim of this project is to analyze Datasets and use algorithms like Support Vector Machine, Naïve Bayes, and K-Nearest Neighbors for prediction. Another aim is to develop a application that allows users to predict diseases utilizing the prediction engine.

Project Implementation Method

In the proposed model, the machine learning  models are applied to the  diseases dataset to predict the risk of these diseases in an individual. An end-to-end process is used where people must enter their details in the application and submit the data. The real-time processing takes place, and the risk is predicted within a few seconds. The mobile application that is used as a real-time database on the cloud is the firebase database. The trained parameters of the model are stored in the database, and prediction is done in real-time.

Methodology:

Disease Prediction has been already implemented using different techniques like Neural networks, decision trees, and the Naïve Byes algorithm.From the analysis, it was found that Naïve Bayes is more accurate than other techniques. So, Disease Predictor also uses Naïve Bayes for the prediction of different diseases.

TOOLS& LIBRARIES:

• React-basedapplication(GUI/Frontend)

• Jupyter notebook
• Anaconda
• Notebook libraries
• Pandas
• NumPy
• Google collab
• Android studio
• Heroku
• Python language
• Keras

Benefits of the Project We are developing a system that provides high accuracy to predict diseases. The disease can be predicted at home so if the disease can be predicted then early treatment can be given to the patients which can reduce the risk of life and save the life of patients and cost. Technical Details of Final Deliverable

Disease Prediction has been already implemented using different techniques like Neural networks, decision trees, and the Naïve Byes algorithm.From the analysis, it was found that Naïve Bayes is more accurate than other techniques. So, Disease Predictor also uses Naïve Bayes for the prediction of different diseases. And we develop an application for user interface to predict diasease.

TOOLS& LIBRARIES:

• React-basedapplication(GUI/Frontend)

• Jupyter notebook
• Anaconda
• Notebook libraries
• Pandas
• NumPy
• Google collab
• Android studio
• Heroku
• Python language
• Keras

Final Deliverable of the Project Software SystemCore Industry MedicalOther IndustriesCore Technology Artificial Intelligence(AI)Other TechnologiesSustainable Development Goals Good Health and Well-Being for PeopleRequired Resources

Item Name	Type	No. of Units	Per Unit Cost (in Rs)	Total (in Rs)
			Total in (Rs)	70000
Server	Equipment	1	20000	20000
machine	Equipment	1	50000	50000