Advancing Type II Diabetes Predictions with a Hybrid LSTM-XGBoost Approach

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.

A composite particle swarm algorithm for global optimization of multimodal functions

During the last decade,many variants of the original particle swarm optimization(PSO)algorithm have been proposed for global numerical optimization,but they usually face many challenges such as low solution quality and slow convergence speed on multimodal function optimization.A composite particle swarm optimization(CPSO)for solving these difficulties is presented,in which a novel learning strategy plus an assisted search mechanism framework is used.Instead of simple learning strategy of the original PSO,the proposed CPSO combines one particle's historical best information and the global best information into one learning exemplar to guide the particle movement.The proposed learning strategy can reserve the original search information and lead to faster convergence speed.The proposed assisted search mechanism is designed to look for the global optimum.Search direction of particles can be greatly changed by this mechanism so that the algorithm has a large chance to escape from local optima.In order to make the assisted search mechanism more efficient and the algorithm more reliable,the executive probability of the assisted search mechanism is adjusted by the feedback of the improvement degree of optimal value after each iteration.According to the result of numerical experiments on multimodal benchmark functions such as Schwefel,Rastrigin,Ackley and Griewank both with and without coordinate rotation,the proposed CPSO offers faster convergence speed,higher quality solution and stronger robustness than other variants of PSO.