EXAMPLE CALL:
/diabetes?Age=45&Gender=Male&Polyuria=No&Polydipsia=No&SWL=Yes&weakness=Yes&Polyphagia=Yes>=Yes&VB=No&Itching=Yes&Irritability=No&DH=Yes&PP=No&MS=No&Alopecia=No&Obesity=No
RES:
{
"value": "['Positive']"
}
curl --location --request GET 'https://zylalabs.com/api/6825/diabetes+risk+assessment+api+using+ai+and+ml/10314/diabetes?Obesity=Required&weakness=Required&VB=Required&SWL=Required&Polyuria=Required&Polyphagia=Required&Polydipsia=Required&PP=Required&Age=Required&MS=Required&Itching=Required&Irritability=Required&Gender=Required>=Required&DH=Required&Alopecia=Required' --header 'Authorization: Bearer YOUR_API_KEY'
{
"Age": "20-...",
"Alopecia": "1.Yes, 2.No.",
"DH": "delayed healing (1.Yes, 2.No.)",
"GT": "Genital thrush (1.Yes, 2.No.)",
"Gender": "1. Male, 2.Female",
"Irritability": "1.Yes, 2.No.",
"Itching": "1.Yes, 2.No.",
"MS": "muscle stiffness (1.Yes, 2.No.)",
"Obesity": "1.Yes, 2.No.",
"PP": "partial paresis (1.Yes, 2.No.)",
"Polydipsia": "1.Yes, 2.No.",
"Polyphagia": "1.Yes, 2.No.",
"Polyuria": "1.Yes, 2.No.",
"SWL": "sudden weight loss (1.Yes, 2.No.)",
"VB": "visual blurring (1.Yes, 2.No.)",
"weakness": "1.Yes, 2.No."
}
curl --location --request GET 'https://zylalabs.com/api/6825/diabetes+risk+assessment+api+using+ai+and+ml/10315/listallvars' --header 'Authorization: Bearer YOUR_API_KEY'
{
"API Info:": "This API requests per values provides a class result ('Positive' or 'Negative') for the individual predicted Diabetes Diagnose Risk. The AI algorithm consists of ML (Machine Learning) techniques for the Classification task. Please contact us for more information about the model. We here cite previous work and acknowledge the involved researchers for providing this Dataset. Citation: 'Early Stage Diabetes Risk Prediction [Dataset]. (2020). UCI Machine Learning Repository.'"
}
curl --location --request GET 'https://zylalabs.com/api/6825/diabetes+risk+assessment+api+using+ai+and+ml/10316/info' --header 'Authorization: Bearer YOUR_API_KEY'
After signing up, every developer is assigned a personal API access key, a unique combination of letters and digits provided to access to our API endpoint. To authenticate with the Diabetes Risk Assessment API Using AI and ML simply include your bearer token in the Authorization header.
| Header | Description |
|---|---|
Authorization
|
Required
Should be Bearer access_key. See "Your API Access Key" above when you are subscribed.
|
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This API requests per value provides a class result ('Positive' or 'Negative') for the individual predicted Diabetes Diagnose Risk. The AI algorithm consists of ML (Machine Learning) techniques for the Classification task. Please contact us for more information about the model. We here cite previous work and acknowledge the involved researchers for providing this Dataset. Citation: 'Early Stage Diabetes Risk Prediction [Dataset]. (2020). UCI Machine Learning Repository.'
The `GET diabetes` endpoint returns a classification of diabetes risk as either 'Positive' or 'Negative' based on the provided health metrics. The `GET listallvars` endpoint returns a list of variables and their possible values, while the `GET info` endpoint provides general information about the API and its functionality.
The key field in the `GET diabetes` response is "value," which indicates the diabetes risk classification. In the `GET listallvars` response, each variable (e.g., Age, Gender) is a key, with its possible values listed as descriptions.
The `GET diabetes` endpoint accepts parameters such as Age, Gender, Polyuria, Polydipsia, SWL, weakness, Polyphagia, GT, VB, Itching, Irritability, DH, PP, MS, Alopecia, and Obesity, allowing users to customize their risk assessment based on individual health metrics.
The response data from the `GET diabetes` endpoint is organized as a JSON object containing a single key, "value," which holds the risk classification. The `GET listallvars` response is structured as a JSON object with variable names as keys and their descriptions as values.
The data used in the Diabetes Risk Assessment API is sourced from the "Early Stage Diabetes Risk Prediction" dataset, available in the UCI Machine Learning Repository. The API acknowledges the contributions of the researchers who provided this dataset.
Typical use cases for this API include healthcare applications assessing diabetes risk for patients, research studies analyzing diabetes prevalence, and wellness programs aiming to provide personalized health recommendations based on individual risk factors.
Users can utilize the returned data by interpreting the risk classification from the `GET diabetes` endpoint to inform healthcare decisions. The variable descriptions from `GET listallvars` can help users understand the input parameters needed for accurate assessments.
Data accuracy is maintained through the use of advanced AI and machine learning algorithms that analyze health metrics. The underlying model is based on a well-established dataset, and users can contact the API provider for more details on the model's validation and performance metrics.
To obtain your API key, first sign in to your account and navigate to the API you want to use. From the API's Pricing section, choose a plan and complete the subscription process. Once subscribed, return to the API page and you will see your API Access Key displayed at the top of the documentation page. You can use this key to authenticate your requests.
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