Application of Machine Learning in Predicting Heart Disease
S. Selvakani *
PG Department of Computer Science, Government Arts and Science College, Arakkonam-631051, India.
K. Vasumathi
PG Department of Computer Science, Government Arts and Science College, Arakkonam-631051, India.
V. Aadhiseshan
PG Department of Computer Science, Government Arts and Science College, Arakkonam-631051, India.
*Author to whom correspondence should be addressed.
Abstract
In the current era of computer science, machines are becoming intelligent and capable of performing tasks like humans. To achieve this, a range of tools, techniques, and methods have been proposed. Support Vector Machines (SVM) is a statistical and computer science model used for supervised learning to analyze data and identify patterns. Logistic regression and random forest methods are primarily used for classification and regression analysis. Similarly, the Random Forest Algorithm (RFA) is a classification algorithm that uses training examples to classify data. In this paper, logistic regression and random forest algorithms are employed to classify data and discover hidden patterns in medical patients' nominal data to predict future diseases. Data mining is utilized to classify and analyze text in the future.
Keywords: Serum Protein Patterns in Pregnancy, Machine Learning (ML), Proteins in gestational ages, supervised learning, Support Vector Machine (SVM), Logistic Regression (LR), Random Forest Algorithm (RFA)