ESWAR_hyderabad- Diabetes analysis and risk calculation – Auto rebuild model by using Flask API - Deep Tech or Machine Learning

[Yaganteeswarudu940]

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ℹ️ Project information

1. Deep Tech or Machine Learning

   As discussed with team iam doing diabetes analysis and risk prediction and Flask API design

2. Project Name: Diabetes analysis and risk calculation – Auto rebuild model by using Flask API

3. Short Project Description: Analyzing Diabetes by using machine learning and deep learning techniques and risk prediction to give suggestions to patients and Flask API Development

4. Team Name: Eswar_hyderabad

5. Team Members: Yaganteeswarudu Akkem

6. Demo Link:

https://github.com/Yaganteeswarudu940/diabetesflaskapi/blob/master/diabetesvideo.mp4

7. Repository Link(s): ----- https://github.com/Yaganteeswarudu940/diabetesflaskapi

8. Presentation Link:
   https://github.com/Yaganteeswarudu940/diabetesflaskapi/blob/master/diabetes_ppt.pptx

9. Azure Services Used- Kindly mention the Azure Services used in your project.

   1. Azure Machine Learning designer
   2. Jupyter notebook
   3. Machine learning CLI
   4. MLFlow
   5. Azure Pipelines

🔥 Your Pitch

Diabetes analysis in many existing systems considered few parameters like age, sex, bmi, insulin, glucose, blood pressure, diabetes pedigree function, pregnancies. But in this paper we considered in addition to age, sex, bmi, insulin, glucose, blood pressure, diabetes pedigree function, pregnancies we included serum creatinine, potassium, GlasgowComaScale, heart rate/pulse Rate,respiration rate,body temparature,low density lipoprotein(LDL),high density lipoprotein (HDL),TG (Triglycerides). Our paper includes analysis of Pima Indian diabetes datasets which is available in UCI machine learning repository, the data set which was acquired from a hospital in Frankfurt, Germany and also visited some local hospitals to get Data sets for diabetes analysis. Due to analysis includes all parameters which causes diabetes, which may be helpful in detecting diseases like heart disease, neuropath, retinopathy, hearing loss, and dementia. This paper main aim is to analyse the datasets by using different machine learning algorithms along with parameter tuning. One more important feature of this article is to analyse diabetes risk factor and based on risk factor providing suggestions to the patients.

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