Cardiovascular Disease Prediction using Various Machine Learning Algorithms

Abstract

Almostone-third of all deaths caused around the world were caused due tocardiovascular diseases. Even if death was not the result, much cost isincurred during the treatment of such diseases. But much of these deaths andtreatments could have been prevented with prior action. Advance knowledge ofthe symptoms and consequently proper care can lead us to avoid such diseases.Thus, current research proposes a highly effective model to predict thepresence of heart diseases.  Bad eatinghabits, smoking, stress, and genetics are some of the factors that influenceour body mechanisms, which actually cause various irregularities in our heartsand thus adversely affect our bodies. The body mechanisms influenced byexternal factors have been included to prepare an efficient model to predictthe probability of cardiovascular diseases. UCI repository dataset has beenutilized for the training and testing purpose in our model. Then accordingly,five different algorithms namely Logistic Regression, Support Vector Machine,Multi-Layer Perceptron (MLP) Classifier with Principal Component Analysis(PCA), Deep Neural Network, Bootstrap Aggregation using Random Forests areexecuted on our filtered dataset to find which one is the optimum out of all ofthem. Pre-processing techniques have been extensively used to filter out thedataset. The data processing along with the different models employed make thisa sound paper, which could be utilized for real-world cases without any priormodification. Different places around the world would take different factorsinto account, hence our model can be used as it takes all critical factors fromseveral datasets.