Author(s)

Ayoub Djama Waberi¹, Ronald Waweru Mwangi², Richard Maina Rimiru³

¹Department of Mathematics, Pan African University Institute for Basic Sciences, Technology and Innovation (PAUSTI), Nairobi, Kenya.
²Department of Computing, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya.
³School of Computing and Information Technology (SCIT), Jomo Kenyatta University of Agriculture and Technology (JKUAT), Nairobi, Kenya.

In this paper, we explore the ability of a hybrid model integrating Long Short-Term Memory (LSTM) networks and eXtreme Gradient Boosting (XGBoost) to enhance the prediction accuracy of Type II Diabetes Mellitus, which is caused by a combination of genetic, behavioral, and environmental factors. Utilizing comprehensive datasets from the Women in Data Science (WiDS) Datathon for the years 2020 and 2021, which provide a wide range of patient information required for reliable prediction. The research employs a novel approach by combining LSTM’s ability to analyze sequential data with XGBoost’s strength in handling structured datasets. To prepare this data for analysis, the methodology includes preparing it and implementing the hybrid model. The LSTM model, which excels at processing sequential data, detects temporal patterns and trends in patient history, while XGBoost, known for its classification effectiveness, converts these patterns into predictive insights. Our results demonstrate that the LSTM-XGBoost model can operate effectively with a prediction accuracy achieving 0.99. This study not only shows the usefulness of the hybrid LSTM-XGBoost model in predicting diabetes but it also provides the path for future research. This progress in machine learning applications represents a significant step forward in healthcare, with the potential to alter the treatment of chronic diseases such as diabetes and lead to better patient outcomes.

LSTM, XGBoost, Hybrid Models, Machine Learning. Deep Learning

Waberi, A. , Mwangi, R. and Rimiru, R. (2024) Advancing Type II Diabetes Predictions with a Hybrid LSTM-XGBoost Approach. Journal of Data Analysis and Information Processing, 12, 163-188. doi: 10.4236/jdaip.2024.122010.