Résumé:
This thesis explores the potential of artificial intelligence in early diabetes prediction,
highlighting innovative contributions while utilizing various databases.
The first two contributions use the "Pima Indian Diabetes Database". An ensemble
model based on the three best methods obtained from supervised machine learning
classification achieves an accuracy of 90.62%, surpassing other state-of-the-art methods. The
contribution continues by focusing on handling missing values, introducing an innovative
imputation approach. The Random Forest model, with an accuracy of 92%, demonstrates the
effectiveness of this method in managing incomplete data.
The subsequent contributions leverage the "Early Stage Diabetes Risk Prediction
Dataset". In the third contribution, an evaluation of seven major classification techniques
reveals that the XGBoost algorithm outperforms its counterparts with a remarkable F1 score
of 94.74% and an accuracy of 96.15%. For the fourth contribution, the crucial challenge of
early diabetes prediction is addressed by balancing data, carefully selecting features, and
applying nine supervised machine learning techniques. The Extra Trees algorithm stands out
with an exceptional accuracy of 97.95%, significantly surpassing other referenced models in
the literature, highlighting the efficiency of the developed approaches in diabetes prediction,
with remarkable precision.
The last contribution is based on the "U.S. Centers for Disease Control and Prevention
dataset" to enhance the performance of prediction models based on survey data. The
presented machine learning approach, especially with the Random Forest model,
demonstrates outstanding performance on a larger test dataset, anticipating the system's
validity in a real-world context.
In conclusion, this thesis proposes a performance-driven approach based on AI for
early diabetes prediction while exploiting different datasets. The exceptional results obtained
underscore the efficiency of the developed models, paving the way for more targeted and
preventive medical interventions, as well as significant improvements to existing diabetes
prediction systems
