EPCAEnhanced Principal Component Analysis for Medical Data Dimensionality Reduction

Title

EPCAEnhanced Principal Component Analysis for Medical Data Dimensionality Reduction

Creator

Vinutha M.R.; Chandrika J.; Krishnan B.; Kokatnoor S.A.

Description

Innovations in technology from thelast one decade have led to the generation of colossal amounts of medical data with comparably low cost. Medical data should be collected with utmost care. Sometimes, the data have high features but not all the features play an important role in drawing the relations to the mining task. For the training of machine learning algorithms, all the attributes in the data set are not relevant. Some of the characteristics may be negligible and some characteristics may not influence the outcome of the forecast. The pressure on machine learning algorithms can be minimized by ignoring or taking out the irrelevant attributes. Reducing the attributes must be done at the risk of information loss. In this research work, an Enhanced Principal Component Analysis (EPCA) is proposed, which reduces the dimensions of the medical dataset and takes paramount care of not losing important information, thereby achieving good and enhanced outcomes. The prominent dimensionality reduction techniques such as Principal Component Analysis (PCA), Singular Value Decomposition (SVD), Partial Least Squares (PLS), Random Forest, Logistic Regression, Decision Tree and the proposed EPCA are investigated on the following Machine Learning (ML) algorithms: Support Vector Machine (SVM), Artificial Neural Networks (ANN), Nae Bayes (NB) and Ensemble ANN (EANN) using statistical metrics such as F1 score, precision, accuracy and recall. To optimize the distribution of the data in the low-dimensional representation, EPCA directly mapped the data to a space with fewer dimensions. This is a result of feature correlation, which made it easier to recognize patterns. Additionally, because the dataset under consideration was multicollinear, EPCA aided in speeding computation by lowering the data's dimensionality and therebyenhancedthe classification model's accuracy. Due to these reasons, the experimental results showed that the proposed EPCA dimensionality reduction technique performed better when compared with other models. 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

Source

SN Computer Science, Vol-4, No. 3

Date

2023-01-01

Publisher

Springer

Subject

Accuracy; Artificial neural networks; Liver cirrhosis; Machine learning; Medical data; Principal component analysis; Singular value decomposition; Support vector machine

Coverage

Vinutha M.R., Department of Information Science and Engineering, Malnad College of Engineering, Hassan, 573202, India; Chandrika J., Department of Information Science and Engineering, Malnad College of Engineering, Hassan, 573202, India; Krishnan B., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Bangalore, 560074, India; Kokatnoor S.A., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Bangalore, 560074, India

Rights

Restricted Access

Relation

ISSN: 2662995X

Format

Online

Language

English

Type

Article

Identifier

https://doi.org/10.1007/s42979-023-01677-5

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85149241829&doi=10.1007%2fs42979-023-01677-5&partnerID=40&md5=d5ae65b601a1f9cd51f46e23a5c42271