README.md

Machine Learning Recommentation System For Diabetes Prediction using oneAPI

A Revolutionary Project for Diabetes Prediction using oneAPI 🌷 Made with

Introduction :

• This project is designed to predict the likelihood of a person developing diabetes based on a number of risk factors. The goal of the project is to help identify individuals who are at high risk for developing the disease so that preventive measures can be taken early on to minimize the likelihood of complication

Problem Statement :

• Diabetes is a chronic disease that affects the body's ability to produce or use insulin, a hormone that regulates blood sugar levels. There are two main types of diabetes: type 1 diabetes, which is usually diagnosed in childhood and is caused by the immune system attacking the cells that produce insulin, and type 2 diabetes, which is usually diagnosed in adulthood and is caused by a combination of genetic and lifestyle factors.

  The effects of diabetes can be serious and long-lasting. High blood sugar levels can damage the blood vessels, nerves, and organs, leading to a range of health complications.

Proposed Solution :

• Machine learning can be used to develop predictive models that can analyze health-related data and identify individuals who are at risk of developing diabetes. These models can use a wide range of data such as age, BMI, glucose levels, insulin levels, and other health-related features to accurately predict the likelihood of diabetes.

  The effects of predicting diabetes using ML are numerous and significant. Firstly, it can help healthcare professionals to identify individuals who are at risk of developing diabetes at an early stage. This allows for early intervention and preventive measures, such as lifestyle changes and medication, to be put in place to manage the disease and reduce its complications.

  Secondly, predicting diabetes using ML can help to reduce the burden of the disease on healthcare systems and the wider society. Early detection and prevention can reduce the number of individuals who require medical intervention for complications arising from diabetes, reducing healthcare costs and improving the quality of life of individuals with diabetes.

  Finally, ML-based diabetes prediction models can also be used to gain insights into the underlying causes of diabetes and how to prevent it. These models can identify the most important factors that contribute to the risk of diabetes, helping healthcare professionals to develop more effective prevention and treatment strategies.

  In summary, predicting diabetes using ML is crucial for early detection and prevention of the disease. It can help to reduce the burden of the disease on healthcare systems and the wider society and provide valuable insights into the underlying causes of diabetes.

Domain :

The Beneficial Users :

• Doctor
• Older Generation
• Pregnant Women
• patients
• Employees
• Hospitals

Value for society :

• The use of a prediction model to identify individuals at risk of developing diabetes using Intel OneAPI can have significant value for society. The benefits of early intervention in diabetes are significant. It can reduce the risk of complications such as heart disease, stroke, kidney disease, blindness, and amputations, which can have a significant impact on the quality of life of individuals and their families. Additionally, early intervention can lead to cost savings for healthcare systems by reducing the need for expensive treatments and hospitalizations.

  By leveraging Intel OneAPI to develop a diabetes prediction model, healthcare professionals can improve patient outcomes and reduce healthcare costs, making it a valuable tool for society as a whole

Value for Business :

• There can be significant value for businesses in using a prediction model to predict diabetes using Intel OneAPI.

  Firstly, diabetes is a major health concern, and businesses in the healthcare industry could benefit greatly from accurate prediction models. For example, healthcare providers could use such a model to identify patients who are at high risk of developing diabetes and provide early intervention and preventive care. This can help reduce healthcare costs and improve patient outcomes.

  Secondly, businesses in the insurance industry could also benefit from a diabetes prediction model. They could use the model to identify individuals who are at high risk of developing diabetes and adjust insurance premiums accordingly. This can help insurance companies reduce their risk and improve their profitability.

  Thirdly, businesses in the food and beverage industry could also use a diabetes prediction model to develop and market products that are specifically targeted towards individuals with diabetes. This can help these businesses expand their customer base and increase their revenue.

Form Factor :

• Our Diabetes Prediction system is in the form of web application to provide this valuable service to the Doctors and society as a whole.

About the Tool:

• Intel OneAPI is a software toolkit that enables developers to build and optimize applications for various hardware platforms, including CPUs, GPUs, and FPGAs. OneAPI includes a suite of tools, libraries, and frameworks that can be used to develop machine learning (ML) applications.

  Using OneAPI, developers can build predictive models for diabetes that can analyze health-related data and identify individuals who are at risk of developing diabetes. These models can use a wide range of data such as age, BMI, glucose levels, insulin levels, and other health-related features to accurately predict the likelihood of diabetes.

Code & Snippet :

👩‍💻 Technology Stack:

Tools

The Team:

### Credit goes to these people: ✨

Contribution :

Made with in India | CHRIST (Deemed to be University)

🤖⚙️Diabetes Prediction using oneAPI⚙️🤖 (Video Link)