A Stacked Predictive Model for Cardiovascular Disease Diagnosis bare

Abstract

The pervasiveness of Cardiovascular disease has rapidly led to it becoming a global threat in the past few years. The pathology causes 18.6 million deaths annually, and its projections are predicted to hike to more than 23 million deaths annually by 2030. The research aimed at developing a CVD predictive model that was more accurate and robust than conventional models. Stacking is one of the efficient methods in machine learning classification tasks that has been widely utilized to fight CVD. The stacking technique offers better solutions by providing a good trade-off between variance and bias. Stacking gives more accurate and robust results. The study compared seven conventional with stacked algorithms and evaluated the algorithms' performance with four evaluation metrics; accuracy, precision, recall, and f1 measure. The better-stacked algorithm was cross-validated with 10 K-folds. The proposed model was achieved: Data description, retrieval, pre-processing, partitioning, normalization, feature selection, stacking, and model evaluation. The stacked algorithms outperformed the conventional algorithms in classification accuracy with 73.62%, recall with 71.24%, and F1 measure with 72.86%. However, in precision, Decision Tree was better performing with 77.41%. Cross-validating, the stacked model with K-Fold, improved the accuracy from 72.76% to 74.71%. The proposed model can be utilized in the primordial prevention strategy of the World Heart Federation. Additionally, medical practitioners can use it as a CVD diagnostic tool. In future works, more data can be retrieved, investigated in multi-level stacking and deep learning, to research the pros and cons of the proposed model.