Prediction of Cardiovascular Disease Using Machine Learning Technique—A Modern Approach

Abstract

Cardio Vascular disease (CVD), involving the heart and blood vessels is one of the most leading causes of death throughout the world. There are several risk factors for causing heart diseases like sedentary lifestyle, unhealthy diet, obesity, diabetes, hypertension, smoking and consumption of alcohol, stress, hereditary factory etc. Predicting cardiovascular disease and improving and treating the risk factors at an early stage are of paramount importance to save the precious life of a human being. At present, the highly stressful life with bad lifestyle activities causes heart disease at a very young age. The main aim of this research is to predict the premature heart disease based on machine learning algorithms. This paper deals with a novel approach using the machine learning algorithm for predicting the cardiovascular disease at the premature stage itself. Support Vector Machine (SVM) is used for segregating the CVD patients based on their symptoms and medical observation. The experimentation results by using the proposed method will facilitate the medical practitioners to provide suitable treatment for the patients on time. A sophisticated model has been developed with the current approach to examine the various stages of CVD and the performance metrics used have given effective and fruitful results as compared to other machine learning techniques.

Cardiovascular disease prediction with machine learning techniques

“…Multivariate analysis in the context of studying CVD involves analyzing multiple variables simultaneously to understand the relationships between them and how they collectively affect CVD risk. 11 This may involve different statistical techniques and modeling strategies depending on the type of data and the specific questions being addressed. [12][13][14] Graph 7 shows a heatmap of a correlation matrix.…”

Cardiovascular Disease Prediction Based on Soft Voting Ensemble Model

“…Kumar et al [8] used a supervised machine learning model Support Vector Machine (SVM) for initial prediction of the presence of cardiovascular disease, and in their research paper, the SVM model provided higher accuracy for predicting cardiovascular disease compared to models such as logistic regression and random forest.…”

Enhancing Breast Cancer Detection and Classification Using Advanced Multi-Model Features and Ensemble Machine Learning Techniques

“…Advanced feature selection methods can help a model employ fewer features, increasing its accuracy and efficiency [18]. To tackle the dramatically increasing cancer rate, early detection and ML technologies are widely used for the diagnosis and prognosis of a variety of ailments, including oral cancer, cardiovascular diseases [19,20], lung cancer [21], diabetes [22,23] and BC [24][25][26][27][28]. The promising results achieved have led scientists to explore the possibility of utilizing data mining as a method for predicting BC recurrence.…”