Diabetics Risk Prediction For Pregnant Women

Table of Contents

-Introduction
-Project Goals
-Data Collection
-Significance
-Getting Started
-Model Used
-Accuracy
-Future Development
-License
-Contact

Introduction

The Diabetic Risk Prediction for Pregnant Women project is an initiative focused on predicting the risk of diabetes during pregnancy, with a primary emphasis on gestational diabetes.

Project Goals

1. Early Detection: The primary goal of this project is to detect the risk of gestational diabetes in pregnant women as early as possible. Early detection empowers healthcare providers and expectant mothers to take proactive measures to ensure a healthy pregnancy.

2. Spread Awareness: Another important goal of the project is to spread awareness of the risk of being attacked by diabetes during pregnancy.

Data Collection:

This dataset is originally from the National Institute of Diabetes and Digestive and Kidney Diseases. The objective of the dataset is to diagnostically predict whether a patient has diabetes, based on certain diagnostic measurements included in the dataset. Several constraints were placed on the selection of these instances from a larger database. In particular, all patients here are females at least 21 years old of Pima Indian heritage.2 From the data set in the (.csv) File We can find several variables, some of them are independent (several medical predictor variables) and only one target dependent variable (Outcome).

Significance:

The Diabetic Risk Prediction for Pregnant Women project is significant for the following reasons:

1. Early Intervention: Early detection allows for timely intervention, reducing the impact of gestational diabetes on both maternal and fetal health.

2. Empowerment: Pregnant women are empowered with information and personalized recommendations, enabling them to take proactive steps toward a healthier pregnancy.

3. Data-Driven Healthcare: The project showcases the potential of data-driven healthcare in improving outcomes and reducing healthcare costs.

Model Used:

The Model used to develop this project is Random Forest Model.andom Forest is a popular machine learning algorithm that can be effectively used for tasks such as prediction, classification, or feature selection which can be crucial in understanding which features are most relevant for predicting or classifying diabetes.

Accuracy:

Accuracy on Test Data: 89.60000000000001% Accuracy on Whole Data: 94.16%

Future Development

Our commitment to the Diabetic Risk Prediction for Pregnant Women project includes ongoing development and enhancements. Future developments may include:

-Calculating the risk percentage of being affected by diabetes. -Providing personalized diet plans. -Integration with electronic health records (EHR) for seamless data sharing with healthcare providers. -Expansion to include a mobile application for convenient access to risk assessment and educational resources. -Collaboration with healthcare institutions for larger-scale data collection and research.

Contact

If you have questions, suggestions, or would like to contribute to the Diabetic Risk Prediction for Pregnant Women project, please contact us at [email protected].