Machine Learning Performance Analysis to Predict Stroke Based on Imbalanced Medical Dataset
This work addresses stroke prediction for healthcare applications, but it is incremental as it applies existing methods to a specific dataset.
The paper tackled the problem of predicting stroke using an imbalanced medical dataset by applying four approaches to improve minority class classification, finding that SMOTE and PCA-Kmeans with DNN-Focal Loss performed best, outperforming Kaggle work by 2-4 times.
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.