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Upgrading the Performance of Machine Learning Based Chronic Disease Prediction Systems using Stacked Generalization Technique

During the past few years, there has been a tremendous increase in mortality due to chronic diseases. Mortality is highest in rural areas of lowand middle-income group countries as there is a lack of affordable and easily accessible healthcare facilities. World Health Organization (WHO) emphasizes on early detection of these diseases to reduce the mortality. In this era of artificial intelligence, machine learning techniques offer promising ways to improve healthcare facilities. Machine learning based prediction tools can be used for early detection of diseases to prevent mortality. This research work has been carried out to build high performance screening tools to diagnose five chronic ailments in early stages. Stacked generalization ensembling approach with tenfold cross validation has been applied over five conventional classifiers. Results obtained in this research work demonstrate that stacked generalization method enhances the performance considerably by reducing variance error. This research work is highly beneficial for primary healthcare centre in rural areas to diagnose diseases in early stages and hence prevent mortality.