Multivariate Data Classification Using Machine Learning

Authors

  • Nirupama KS, Akram Pasha, Greeshma Reddy, Sree Chandana, Roopa

Abstract

Nowadays, data analytics has become one of the major business tools to gain insights into the data and make many important business decisions. The applications of data analytics are innumerable spawning major applications in various domains including healthcare. Machine Learning (ML) is one of the major tools that drive any data analytics application. Therefore, in this paper, an effort is made to classify the Liver Disease (LD) data set having multiple dimensions of attributes. The data set comprises the 583 observations taken from the liver disease patients and employed Minmax feature scaling to normalize the data. The data set was split into training and testing set in the ratio of 80% and 20% respectively. The training set was trained on Support Vector Machine (SVM), Decision Tree (DT), Logistic Regression (LR), k-Nearest Neighbor (k-NN) classifiers to investigate the best classification model giving maximum accuracy. Amid all the ML classifiers involved, k-NN provides 80% of maximum classification accuracy.

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Published

2020-05-16

Issue

Section

Articles