Performance Evaluation Of Machine Learning Algorithms For Disease Prediction

  • Dr. K. Vijaya Kumar, Mr. Imandi Raju and Mrs. Pravallika Rudraraju

Abstract

Machine learning algorithms use an assortment of measurable, probabilistic and streamlining
methods to pick up from past comprehension and distinguish valuable examples from massive,
structured, unstructured and complex datasets. Data mining and Machine learning assist with
foreseeing all kinds of diseases more effectively and help treat patients. Predictive analysis with
the assistance of efficient multiple machine learning algorithms helps to predict the disease more
correctly to treat patients in a better way. Using machine learning algorithms can lead to rapid
disease prediction with high accuracy. The most objective of the work is to focus principally on
the performance analysis of predicting disease through various supervised machine learning
algorithms on pathological data. Relative investigation of the different performances of machine
learning algorithms is finished through the collection of symptoms for disease and graphical
representation of the results. In the proposed system, we present a GUI model, which is used by
Naïve Bayes method, Decision Tree method, Support Vector Machine method and Random
Forest method, the four diverse machine learning algorithms for disease prediction. The
evaluating and comparative study between four different machine learning classification
algorithms is made using performance metrics such as accuracy, precision, recall and F-score.
The approach includes four steps. Firstly, important clinical features are selected. Secondly, an
input of symptoms is taken from the user. Third, machine learning algorithms are applied for
classifying disease based on these clinical features. Finally, we compare the results obtained by
different machine learning classification algorithms and visualize the result in a graph. In this
way with the expanding accessibility of electronic health data, increasingly hearty and
progressed computational methodologies, for example, machine learning has gotten
progressively functional to apply and investigate in disease prediction zone.

Published
2020-06-01
How to Cite
Dr. K. Vijaya Kumar, Mr. Imandi Raju and Mrs. Pravallika Rudraraju. (2020). Performance Evaluation Of Machine Learning Algorithms For Disease Prediction. International Journal of Advanced Science and Technology, 29(7), . 7820-7830. Retrieved from http://sersc.org/journals/index.php/IJAST/article/view/24324
Section
Articles