Author: Mohan Raja Pulicharla
The heart, as the second most vital organ after the brain, is integral to maintaining bodily equilibrium, and disruptions to its function have profound health consequences. Heart disease, a leading global cause of mortality, often arises from cumulative daily physiological changes, emphasizing the importance of timely illness prediction. In healthcare, the fusion of data mining and machine learning, explored in this study using Support Vector Machine, Decision Tree, and Random Forest algorithms, addresses the challenges of diagnosing prevalent conditions like heart disease, particularly crucial in the field of cardiology.
Our proposed machine learning-based approach for diagnosing cardiac disease employs a range of classification algorithms and advanced feature selection techniques, demonstrating superior accuracy in detecting heart diseases from extensive datasets of unprocessed medical images. This technological advancement holds the potential to significantly enhance patient care in various healthcare settings, showcasing the promising impact of artificial intelligence tools on improving the quality of life for billions worldwide.
After the brain, the heart is regarded as the second-most significant organ. Every heart disruption causes the entire body to become upset. Heart disease is one of the top five killer diseases in the world. Disorders, including heart disease, are a result of the changes that occur to us daily. Consequently, it is crucial to predict a sickness at the appropriate time. Data mining is a fundamental and fundamental process for defining and discovering relevant data and uncovering hidden patterns in massive databases. By predicting and diagnosing various diseases, data mining, and machine learning techniques are used in the medical sciences to address genuine health-related challenges. This study compares the performance of three machine learning algorithms-support vector machine, decision tree, and random forest-for the prediction of heart disease.
After the brain, the heart is regarded as the second-most significant organ. Every heart disruption causes the entire body to become upset. Heart disease is one of the top five killer diseases in the world. Disorders, including heart disease, are a result of the changes that occur to us daily. Consequently, it is crucial to predict a sickness at the appropriate time. Data mining is a fundamental and fundamental process for defining and discovering relevant data and uncovering hidden patterns in massive databases. By predicting and diagnosing various diseases, data mining, and machine learning techniques are used in the medical sciences to address genuine health-related challenges. This study compares the performance of three machine learning algorithms-support vector machine, decision tree, and random forest-for the prediction of heart disease.
Machine Learning-Based Approach for Diagnosing Cardiac Disease
Various algorithms of artificial intelligence and Machine Learning applied in cardiovascular medicine.
The study emphasizes the critical need for swift and accurate heart disease identification, proposing a machine learning approach with Support Vector Machines, Logistic Regression, Artificial Neural Networks, K-Nearest Neighbors, Naive Bayes, and Decision Trees for classification. Efficiency is enhanced through feature selection algorithms and a conditional mutual information method, ensuring commendable accuracy, particularly with Support Vector Machines. This makes it a promising tool for rapid implementation in medical settings, crucial for early identification and interrupting cardiac disease progression. The analysis of diverse datasets identifies key features for heart disease prediction, utilizing seven machine learning methods. A hybrid dataset is created and analyzed with Python’s Scikit-learn module using a univariate feature selection technique, offering a comprehensive approach to discern crucial factors in predicting and preventing heart disease.
Research Article Link: https://juniperpublishers.com/jocct/JOCCT.MS.ID.556004.php