- Diabetes is a chronic condition in which the body is unable to properly regulate its blood sugar levels. It is a common condition, affecting millions of people worldwide.
- Early diagnosis and treatment of diabetes can help prevent complications and improve the overall health of patients. However,
- Accurately diagnosing diabetes can be challenging, as there are many factors that can affect blood sugar levels, and the symptoms of diabetes can vary widely among individuals.
Can you build a machine learning model to accurately predict whether or not the patients in the dataset have diabetes or not?
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Build a machine learning model that can accurately predict whether or not a patient has diabetes, based on a given dataset of patient information.
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The model should be able to make predictions with high accuracy
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in order to help healthcare providers diagnose and treat diabetes more effectively
Decision Tree and XGBoostachieving 100% accuracy on the training data indication that the model may be overfittingLogistic Regression machinelearning achieving 80% training data and achieving 78% accuracy on the test data it means that in ~8 out of 10 cases, the model's predictions will be correct. This is generally considered to be a good level of accuracy, but it is not perfect.- Collect more data and use it to train model
- Keep experimenting and trying new things to see what works best.
- Balancing Data
- Using ANN Algoritm and apply frameworks such as Keras & TensorFlow