Classification and Prediction Analysis of Diseases and Other Datasets Using Machine Learning

Abstract

Classification is one of the most used machine learning technique especially in the prediction of daily life things. Its first step is grouping, dividing, categorizing, and separation of datasets based on future vectors. Classification procedure has many algorithms, some of them are Random Forest, Naïve Bayes, Decision Tree and Support Vector Machine. Before the implementation of every technique, the model is created and then training of dataset has been made on that model. Learning the algorithm-generated model must be fit for both the input dataset and forecast the records of class label. Many models are available for prediction of a class label from unknown records. In this paper, different classifiers such as Linear SVM, Ensemble, the Decision tree has been applied and their accuracy and time analyzed on different datasets. The Liver Patient, Wine Quality, Breast Cancer and Bupa Liver Disorder datasets are used for calculating the performance and accuracy by using 10 cross-fold validation technique. In the end, all the applied algorithm results have been calculated and compared in the terms of accuracy and execution time.