Machine Learning Performance Analysis to Predict Stroke Based on Imbalanced Medical Dataset
Cerebral stroke, the second most substantial cause of death universally, has
been a primary public health concern over the last few years. With the help of
machine learning techniques, early detection of various stroke alerts is
accessible, which can efficiently prevent or diminish the stroke. Medical
dataset, however, are frequently unbalanced in their class label, with a
tendency to poorly predict minority classes. In this paper, the potential risk
factors for stroke are investigated. Moreover, four distinctive approaches are
applied to improve the classification of the minority class in the imbalanced
stroke dataset, which are the ensemble weight voting classifier, the Synthetic
Minority Over-sampling Technique (SMOTE), Principal Component Analysis with
K-Means Clustering (PCA-Kmeans), Focal Loss with the Deep Neural Network (DNN)
and compare their performance. Through the analysis results, SMOTE and
PCA-Kmeans with DNN-Focal Loss work best for the limited size of a large severe
imbalanced dataset,which is 2-4 times outperform Kaggle work.