Ideaz

In India, getting a diabetes diagnosis is frequently a stressful and delayed process. There may be a lot of stress during the time between identifying the first symptoms, making an appointment with a doctor, and eventually receiving the results of a blood test. Many people put off this important step because they find it inconvenient or are afraid of needles, which results in a large number of undiagnosed or pre-diabetic cases in our communities.

AI from Diabetes Health is here to change that. We are creating a concise, targeted mobile application that offers a quick, initial Type 2 diabetes risk assessment. Our app provides an immediate understanding of a user's possible risk by utilizing a machine learning model that evaluates basic, non-invasive information such as a user's height, weight, and BMI. Our goal is to remove people's reluctance to get tested in the first place and enable them to take the vital first step toward promptly seeking professional medical advice.

The Issue:

Anxiety and Diagnostic Delays: Diabetes cannot be tested instantly using the conventional method. Waiting for blood test results can be extremely stressful and postpone the start of necessary lifestyle changes or medical consultations.

Testing Hesitancy: Because blood tests can be uncomfortable, expensive, or inconvenient, many people steer clear of preventative screening. This results in a sizable population that is undiagnosed and ignorant of their risk.

Absence of an Accessible First Step: Self-concern and clinical diagnosis are not the same thing. People need a quick, easy, and private tool to help them determine whether seeing a doctor should be their top priority.

The "Diabetes Health AI" mobile application is our MVP solution.

Our MVP:

 It is simple, intuitive mobile application with a single primary purpose:

Instant Risk Assessment: After launching the app, the user inputs their age, height, and weight, among other basic information. Their BMI is automatically determined by the app, which then feeds the results into our in-house machine learning model.

Clear, Simple Results: Within seconds, the app displays a clear, easy-to-understand preliminary risk assessment, categorized into levels such as "Low Risk," "Medium Risk," or "High Risk."

Practical Advice: In addition to the outcomeThe app makes it clear that it is a screening tool and not a diagnostic one, and it strongly advises talking with a doctor about the results.

Important Note: The Diabetes Health AI app is not a replacement for a medical diagnosis; rather, it is a preliminary risk indicator. It should not be used in place of professional medical evaluation and testing; rather, it should be used to encourage users to do so.

Technical Details:
Hardware elements:

Smartphone of the user: The only hardware needed for the application is a typical smartphone because it is fully software-based.

Core Predictive Model (Classification Model): AI Components

A machine learning model, most likely a Support Vector Machine (SVM) or Logistic Regression model, is at the core of our application. It was selected due to its demonstrated accuracy in binary classification tasks.

To identify trends between input features—Age, Height, Weight, and BMI—and the risk of diabetes, the model is trained on well-known, anonymized medical datasets (like the Pima Indians Diabetes Database).

Mechanism of Continuous Learning:

By periodically retraining, the model is intended to "learn from its mistakes" and get better over time.

Users will have the option to report the results of their official clinical diagnosis through an optional, anonymous feedback loop. The model is routinely retrained and improved using this fresh, validated data, improving its accuracy and dependability for all users.

The information gathered will be crucial for:
Enhancement of Iterative Models: Building a strong feedback system is the main application of the data. We can improve the algorithm's accuracy, lower its margin of error, and increase the precision of the risk predictions with each new dataset.

Public Health Insights: Anonymized aggregate data can be used to find associations between a user's physical characteristics (such as particular BMI ranges) and their risk of developing diabetes. This information is useful for public health research.

Targeted Awareness: Public health organizations can develop more successful and focused diabetes prevention awareness campaigns by having a better understanding of the general risk profiles of our user base.

 Conclusion :

Diabetes Health AI aims to get more people to see doctors sooner rather than to replace them. Our goal is to make the first step toward health awareness quick, easy, and stress-free. Millions of people will be able to take immediate control of their health by removing the initial barrier of testing hesitancy and establishing a link between professional diagnosis and personal concern