Diabetes Detection System using PHP and MYSQL

Diabetes Detection System using PHP and Mysql is a web-based technology that allows users to predict the likelihood of diabetes by entering key medical parameters, including Age, Glucose Level, Blood Pressure, and BMI (Body Mass Index). The system analyses the data through a rule-based logic or predictive model and provides results in an easy-to-understand format, helping raise awareness and encourage early detection. In this web application user must register. The primary objective of this application is to assist users in predicting the risk of diabetes and maintaining a record of diabetes-related health parameters for themselves and their loved ones.

In this project, we use PHP and MySQL database, and it has two module,e i.e. Admin and User

Admin Module

1. Dashboard: This section of the web application provides the admin with a comprehensive summary of user activity and system usage. It includes real-time statistics related to diabetes predictions and user registrations, offering valuable insights into how the system is being used like Today’s Prediction, Yesterday’s Prediction, Last 7 Days’ Predictions, Total Prediction and total registered users.
2. Reg User’s: In this section, the admin can view the diabetes prediction records along with the personal details of all registered users.
3. Reports: In this section, admin can generate below reports

• Between dates prediction report
• Registered Users Report

4. Profile: This section allows admin to update and manage their personal profile information.
5. Change Password: This section enables users to securely update and manage their account password.
6. Logout: This section allow users to securely exit their account after using the system

User Module

1. Dashboard: This section of the web application allows users to conveniently view a summary of their diabetes prediction history like Today’s Prediction, Yesterday’s Prediction, Last 7 Days’ Predictions and Total Prediction.
2. Diabetes Detection: This section enables users to assess the likelihood of diabetes by entering key health indicators into the system. The input fields include: Age, Glucose Level, Blood Pressure, and Body Mass Index (BMI).
3. B/W Dates Report: This section allows users to generate detailed reports of diabetes prediction data entered within a specific date range. By selecting a start date and an end date, users can filter and view all prediction records submitted during that period.
4. Profile: This section allows users to update and manage their personal profile information.
5. Change Password: This section enables users to securely update and manage their account password.
6. Logout: This section allows users to securely exit their account after using the system.