Principal Component Analysis for Disease Diagnosis

Volume 6 Issue 2 March - May 2017

Research Paper

Principal Component Analysis for Disease Diagnosis

Juby Mathew*, R Vijayakumar**

* * Assistant Professor, Department of MCA, Amal Jyothi College of Engineering, Kanjirappally, Kerala, India.

** Professor and Dean, School of Computer Sciences, Mahatma Gandhi University, Kottayam, Kerala, India.

Mathew, J., and Kumar, R. V. (2017). Principal Component Analysis for Disease Diagnosis. i-manager’s Journal on Information Technology, 6(2), 1-7. https://doi.org/10.26634/jit.6.2.13569

Abstract

The new generation of personal authentication technologies based on individual biological characteristics is the core of various applications of the real or the virtual society. The volume of the medical data is increasing due to the presence of vast amount of features, the conventional rule mining technique is not competent to handle the data and to perform precise diagnosis. For instance, this paper intends to implement the improved rule mining technique to overcome limitations. The proposed method has two main contributing stages. The first stage is the robust feature extraction process using the improved Multi-Linear Principal Component Analysis (PCA), whereas the second stage is the classification process using Support Vector Machine. Principal Components Analysis (PCA) is the most commonly-used dimensionality reduction technique employed for the feature extraction of neural spikes.