Copyright : © the author(s), publisher and licensee Technoscience Academy. This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial License, which permits unrestricted non- commercial use, distribution, and reproduction in any medium, provided the original work is properly cited International Journal of Scientific Research in Science, Engineering and Technology Print ISSN: 2395-1990 | Online ISSN : 2394-4099 (www.ijsrset.com) doi : https://doi.org/10.32628/IJSRSET207542 150 Classification techniques based on Artificial immune system algorithms for Heart disease using Principal Component Analysis Kirti Bala Bahekar Research Scholar, Barkatullah University Bhopal, Madhya Pradesh, India Article Info Volume 7 Issue 5 Page Number: 150-160 Publication Issue : September-October-2020 Article History Accepted : 20 Sep 2020 Published : 27 Sep 2020 ABSTRACT The modern era is a period of machine learning, which helps in finding new facts for future predictions. Classification is a machine learning tool that helps in the discovery of knowledge in Big data and it has various potential applications. Researchers nowadays are more inclined to the techniques which are inspired by nature. The artificial immune system (AIS) is such a method that is originated by the qualities of the humanoid immune system. In the proposed method, artificial immune stimulated classifiers as supervised learning methods are used for classifying Heart disease datasets. The performance of the classifiers strongly depends on the datasets used for learning. Here it is observed that, when the principal component analysis is performed on the standard dataset, then classifiers' accuracy and other facts show improvement in performance, which leads to fall in errors. Keywords: Classification, Machine learning, Supervised learning,Artificial immune system, Principal Component Analysis. I. INTRODUCTION Big data analysis is the trending and immerging field of computer sciences. In the modern computational world, Machine learning [19] and deep learning are used as tools for data analysis and data mining applications. Data mining [11] is the methodology applied in the area of information generation, which can be utilized in the future for long term decisions. Classification is the data mining method of predicting the nature of the item, based on the data given related to the other entities. Classification can be done by using various methods like decision tree, rule-based methods, memory-based methods, Bayesian network, neural network, etc. New nature motivated technologies are immerging nowadays, where researchers are interested in the application of those techniques in the computational field. The field of Artificial Intelligence [11] is explored into various techniques for improvement in the performance of the system, such concepts can be like genetic algorithm, swarm intelligence, fuzzy logic, and soft computing i.e. artificial neural network [13] and artificial immune system [17]. The proposed method contains various artificial immune system (AIS) algorithms as classification techniques for predicting class attributes of the dataset. Effectiveness of various AIS classifier algorithms with a combination of principal component analysis (PCA)