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chronic-kidney-disease-prediction-project's Introduction

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To install the project on your local machine,

  • First of all create a database in your local machine i.e MySQL Database with name "project_orchid" which you can change manually and add another database by changing DB_Database name which you have in your project
  • Goto the folder where you have cloned the project
  • Open the terminal inside the folder and run the following command
./projectInitialize.sh

And the project will be installed in your system and to start the server php artisan serve

About Chronic Kidney Disease Prediction Using Naive Bayes ALgorithm

The Chronic Kidney Disease Prediction system is a web-based system that helps patient keep track of their kidney health. It works by collecting information about the patient's health and using data mining algorithms to analyze the information and make predictions about the patient's condition. This system helps patient to make faster and more accurate diagnoses, and can be used from a distance. The goal is to make it easier for patient to keep track of their health and help them understand their medical status.

##Problem Statement • People with chronic diseases have a higher risk of health problems and unequal access to healthcare. • Lack of technology to monitor and track health care for these diseases makes it difficult and time-consuming. • Health measurements and records are often recorded on paper, which can lead to missing information. • Patients with chronic diseases struggle to choose diets and exercise plans that are right for them. • Society needs to improve health care and outcomes for people with chronic diseases

Objective

• To reduce the chances of CKD leading to dialysis or kidney transplantation. • Replace paper measurement recording with a database in the application • Allow patients to view medical records and progress • Use data mining and machine learning algorithms to build the model • Focus on chronic kidney diseases by using Naive Bayes algorithms. • Provide experimental results and analysis to measure accuracy

Design

Use case diagram

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Contributing

Thank you for considering contributing to the Laravel framework! The contribution guide can be found in the Laravel documentation.

Code of Conduct

In order to ensure that the Laravel community is welcoming to all, please review and abide by the Code of Conduct.

Security Vulnerabilities

If you discover a security vulnerability within Laravel, please send an e-mail to Taylor Otwell via [email protected]. All security vulnerabilities will be promptly addressed.

License

The Laravel framework is open-sourced software licensed under the MIT license.

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