An Optimized Hybrid Fuzzy Weighted k-Nearest Neighbor to Predict Hospital Readmission for Diabetic Patients

Predicting hospital readmissions among diabetic patients has been of substantial interest to many researchers and health decision makers to provide quality and cost effective health care. In this paper, we optimize an efficient classifier for hospital readmission prediction within 30 days of discharge, the hybrid fuzzy weighted k-nearest neighbor (HFWkNN) method. The optimization is performed through the hyperparameter γ, ε and εmin introduced in the membership function of HFWkNN. These hyperparameters are important since they directly control the behavior of the training phase and significantly affect the performance of the method. A relationship is established between the performance of the proposed HFWkNN and the hyperparameters using two powerful optimization algorithms; grid search and random search. Experimental results show improved performance using the optimized hyperparameters in the resulting hospital readmission prediction model. To show that HFWkNN model can be generalized, the results are compared with those of several kNN-based algorithms using two additional classification datasets in addition to the hospital readmission dataset. They are IRIS dataset and breast cancer dataset. These are common benchmark sets with real-world data. The model so far achieved higher classification accuracy than FkNN model. The best hyperparameter values for HFWkNN with grid search are γ=0.2249, ε=1.112 and ε _min =0.01. Also, HFWkNN shows a performance of 80.00%, meaning that it has generalized well on the different data sets.