https://github.com/shakilgithub20/diabetes-prediction
Using machine learning to determine whether a patient has diabetes or not. applied data cleansing, modeling, visualization.
https://github.com/shakilgithub20/diabetes-prediction
kares loss-functions machine-learning neural-network nnfl optimizer pandas python sklearn tensorflow
Last synced: 3 months ago
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Using machine learning to determine whether a patient has diabetes or not. applied data cleansing, modeling, visualization.
- Host: GitHub
- URL: https://github.com/shakilgithub20/diabetes-prediction
- Owner: Shakilgithub20
- Created: 2021-09-25T12:13:12.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2022-06-26T05:58:20.000Z (almost 3 years ago)
- Last Synced: 2025-02-01T05:31:33.693Z (4 months ago)
- Topics: kares, loss-functions, machine-learning, neural-network, nnfl, optimizer, pandas, python, sklearn, tensorflow
- Language: Jupyter Notebook
- Homepage: https://nbviewer.org/github/Shakilgithub20/Diabetes-Prediction/blob/main/Diabetes_Prediction_Accuracy_94_46_ipynb.ipynb
- Size: 1.43 MB
- Stars: 8
- Watchers: 1
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Diabetes Prediction using Machine Learning
Statistical models to predict incident are often based on variables, Here, I pursued some main goal. Such as, I train an artificial neural network with dataset and predict the diabetes(Target value of 0/1).
# Details about the dataset:
The datasets consists of several medical predictor variables and one target variable, Outcome.
1) Preg = Number of times pregnant.
2) GLU = Plasma glucose concentration a 2 hours in an oral glucose tolerance test
3) BP = Diastolic blood pressure (mm Hg)
4) ST = Triceps skin fold thickness (mm)
5) INS = 2-Hour serum insulin (mu U/ml)
6) BMI = Body mass index (weight in kg/(height in m)^2)
7) DPF = Diabetes pedigree function
8) Age = Age in years
9) Outcome = 1 - YES (meaning the patient might Diabetes); 0 - NO (the patient doesn't Diabetes).
Number of Observation Units: 768.
Variable Number: 9