A Data-Driven Approach for Building a Cardiovascular Disease Risk Prediction System

Cardiovascular disease is a leading cause of mortality worldwide. The disease can develop without showing apparent symptoms at an early stage, making it difficult for domain experts to provide intervention. Using machine learning techniques in chronic disease prediction is becoming popular because of their ability in processing a large amount of data and analysing the patterns buried in the datasets. To increase the accessibility for healthcare professionals to ready-to-use machine learning prediction pipelines, we introduce an automated machine learning system called Auto-Imblearn that can process and analyse the imbalanced clinical data; automatically compare different classification algorithms and apply the best algorithm for prediction. Using a real patient dataset, the prediction of our proposed system achieves the best performance against the state-of-the-art baselines while saving significant computations from the exhaustive approach.