Cardiovascular Disease Prediction using Various Machine Learning Algorithms

Abstract

Almost one-third of all deaths caused around the world were caused due to cardiovascular diseases. Even if death was not the result, much cost is incurred during the treatment of such diseases. But much of these deaths and treatments could have been prevented with prior action. Advance knowledge of the symptoms and consequently proper care can lead us to avoid such diseases. Thus, current research proposes a highly effective model to predict the presence of heart diseases. Bad eating habits, smoking, stress, and genetics are some of the factors that influence our body mechanisms, which actually cause various irregularities in our hearts and thus adversely affect our bodies. The body mechanisms influenced by external factors have been included to prepare an efficient model to predict the probability of cardiovascular diseases. UCI repository dataset has been utilized 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 are executed on our filtered dataset to find which one is the optimum out of all of them. Pre-processing techniques have been extensively used to filter out the dataset. The data processing along with the different models employed make this a sound paper, which could be utilized for real-world cases without any prior modification. Different places around the world would take different factors into account, hence our model can be used as it takes all critical factors from several datasets.

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