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The main objective of the current research is to use machine learning techniques to accurately and timely detect diabetes. The aim is to build models by means of patient data analysis that can properly predict diabetes status, so enhancing the capacity for early stage disease diagnosis and management.
Methodology:
- Data collecting: The PIMA Indian Diabetes database comprising body mass index (BMI), arterial pressure, insulin quantity, and glucose concentration
- Data preparation for model training: addressing missing values, feature normalizing,
- Different classification techniques including logistic regression, decision tree, random forest as well as gradient boosting include model selection.
- Training each algorithm using the dataset by modifying hyperparameters to get best possible performance.
- Examining a model’s performance with accuracy, precision, recall, F1-score, ROC-AUC criteria
Forecasting diabetes, the Gradient Boosting classifier demonstrated highest accuracy. This study underlines the need of the relevance of carefully selecting and evaluating models since it demonstrates the way that how machine learning can assist in timely identification of diseases in the medical field.
