Diabetes Prediction Using Machine Learning

This repository contains a Jupyter notebook for building machine learning models to predict the presence of diabetes in Pima Indian women based on various diagnostic measurements. The dataset used in this project is the Pima Indians Diabetes Database, which is originally from the National Institute of Diabetes and Digestive and Kidney Diseases.

Dataset

The dataset consists of several medical predictor variables and one target variable, Outcome, which indicates whether a patient has diabetes or not. The predictor variables include:

• Number of pregnancies
• Plasma glucose concentration
• Diastolic blood pressure
• Triceps skin fold thickness
• 2-Hour serum insulin
• Body mass index (BMI)
• Diabetes pedigree function
• Age
• Outcome(indicating diabetes or not)

The dataset is available in the data directory of this repository as diabetes.csv.

Requirements

To run this project, you'll need the following dependencies:

• Python (version 3.6 or later)
• NumPy
• Pandas
• Scikit-learn
• Matplotlib
• Seaborn
• Imbalanced-learn (for handling imbalanced data)
• SciPy

Code Analysis

The Diabetes.ipynb notebook performs the following tasks:

1. Data Loading and Preprocessing:

   • Loads the dataset from the diabetes.csv file.
   • Handles missing values by imputing with the minimum value or using KNNImputer.
   • Uses IterativeImputer as well to handle missing values but KNNImputer gives better results.
   • Scales the features using StandardScaler, RobustScaler and MinMaxScaler (although it doesn't affect the model performance).
   • The 'Pregnancies' feature was dropped from the dataset as it was deemed potentially irrelevant to predicting diabetes.
   • Performs exploratory data analysis, including visualizations and statistical summaries.

2. Model Training and Evaluation:

   • Splits the data into training and testing sets.
   • Trains a logistic regression model with different solvers and hyperparameters.
   • Trains a support vector machine (SVM) model with different kernels.
   • Trains a KNeighborsClassifier with different hyperparameters.
   • Uses GridSearchCV to find the best values for the hyperparameters.
   • Evaluates the models' performance using metrics like accuracy, classification report, confusion matrix, and ROC-AUC score.

3. Visualization:

   • Generates visualizations, such as scatter plots, histograms, heatmaps, and ROC curves, to assess the models' performance and data distribution.

4. Handling Imbalanced Data:

   • Attempts to handle imbalanced data using oversampling with SMOTE (Synthetic Minority Over-sampling Technique), but it doesn't improve the model's performance.

Conclusion

• Logistic regression, SVM models and KNeighborsClassifier were trained to predict diabetes based on health metrics.

• The logistic regression model with the 'liblinear' solver exhibited robust performance, achieving an AUC-ROC of 0.80 and an accuracy of 84% in predicting diabetes based on health metrics.

• Support Vector Machine (SVM) models demonstrated comparable performance, with the 'linear' kernel performing slightly better than other kernels. The SVM models attained an accuracy of 81% and an AUC-ROC of 0.76.

• The KNeighborsClassifier model showed the least accurate results, with an AUC-ROC of 0.69 and an accuracy of 77%.

Contributing

Contributions to this project are welcome. If you find any issues or have suggestions for improvements, please open an issue or submit a pull request.