Project Summary
This dataset is originally from the National Institute of Diabetes and Digestive and Kidney Diseases. The objective of the project is to diagnostically predict whether a patient has diabetes, based on certain diagnostic measurements included in the dataset. Several constraints were placed on the selection of these instances from a larger database. In particular, all patients here are females at least 21 years old of Pima Indian heritage.From the data file we can find several variables, some of them are independent (several medical predictor variables) and only one target dependent variable (Outcome).
Project Overview: Utilized Python to develop a machine learning model to analyze and predict the likelihood of diabetes based on patient data. The primary objective was to identify key factors contributing to diabetes and build a predictive model to assist in early diagnosis and intervention.
- DELIVERABLES
- Utilized Python to develop a machine learning model for predicting diabetes risk, utilizing advanced algorithms to provide accurate and actionable health insights.
- Collected and preprocessed a comprehensive dataset, including patient demographics, medical history, and biometric measurements, to ensure high-quality input for model training.
- Implemented various machine learning algorithms, such as logistic regression, decision trees, and random forest methods, to evaluate and select the most effective approach for prediction.
- Applied feature selection techniques to identify key predictors of diabetes, including blood glucose levels, BMI, age, and family history, enhancing model performance and interpretability.
- Utilized cross-validation and hyperparameter tuning to optimize model performance, achieving high accuracy, precision, and recall in predicting diabetes risk.
- Developed a robust evaluation framework using metrics such as ROC-AUC, confusion matrices, and F1-scores to assess model effectiveness and reliability.
- Created detailed visualizations and reports to communicate prediction results, model performance, and risk factors to stakeholders and medical professionals.
- Compared various models to arrive at a model with an accuracy of 76%.
- ANALYSIS IMPACT
- The model's ability to accurately forecast risk levels supports proactive health management and contributes to better patient outcomes.