❗ still development
Diadetect is an application designed to detect the likelihood of diabetes based on user-input data. The application utilizes a TensorFlow neural network for its predictive model and is built for mobile platforms using React Native.
- User-Friendly Interface: Intuitive and easy-to-use design for seamless user interaction.
- Predictive Model: Powered by a TensorFlow neural network, DiaDetect provides accurate predictions based on input parameters.
Input Parameters:
| 📛 Name | ❓ How to get | 📖 Description |
|---|---|---|
Pregnancies |
This information is typically gathered from medical records or the patient's medical history. Patients can provide details about their previous pregnancies during medical consultations. | The number of pregnancies a woman has experienced. |
Glucose levels |
Glucose levels are measured through blood tests. Patients can undergo blood tests at healthcare laboratories or use home blood glucose meters for self-monitoring. | The concentration of glucose (sugar) in the blood. |
Blood pressure |
Blood pressure measurement is commonly done using a sphygmomanometer. This test can be performed at healthcare facilities, clinics, or even at home using digital blood pressure monitors. | The force of blood against the walls of the arteries, usually measured in millimeters of mercury (mmHg). |
Skin thickness |
Measurement of skinfold thickness is typically done by healthcare professionals using a tool called a skinfold caliper. | Refers to the thickness of a fold of skin at a specific location on the body. |
Insulin levels |
Insulin levels are measured through blood tests conducted in healthcare laboratories. | The amount of insulin in the blood, a hormone crucial for regulating blood sugar. |
BMI (Body Mass Index) |
BMI is calculated based on weight and height measurements. Weight can be measured using scales, and height can be measured using a stadiometer. | A numerical value of a person's weight in relation to their height. |
Diabetes pedigree function |
Information about the family history of diabetes is obtained from the patient. Patients provide details about whether any family members have a history of diabetes. | A function presenting the family history of diabetes and estimating the genetic risk associated with diabetes. |
Age |
Age information is obtained directly from the patient during medical consultations or can be derived from personal identification data. | The age of the individual. |
- Node.js and npm installed
- Python
coming soon