A new predictive medical approach based on data mining and Symbiotic Organisms Search algorithm

Abstract

Handling very large data, in order to make the best decision, is only possible through an extraction of knowledge. Data mining has become a widely used process in data analytics to extract the most important knowledge for predictive decision making. One of the important types of data mining is clustering mechanism; its purpose is dividing data into a set of clusters with very large data, the numbers of parameters are very high, and the clustering problem is more difficult. Metaheuristics have been widely used in clustering; they can provide satisfactory solutions for complex problems. The main objective of this paper is to propose a new clustering algorithm based on a metaheuristic technique called Symbiotic Organisms Search (SOS), it was inspired from a biological process, and it simulates the symbiotic interaction between organisms of the same population. The SOS method is used to find the optimal centers of a number of clusters, as a supervised data mining technique. Experimental results have been performed through two phases. Firstly, the SOS technique is benchmarked with six well-known test functions. Secondly, different medical datasets have been used to test our proposed clustering method based on SOS, and show its credibility of treatment.

KEYWORDS:

• Symbiotic
• data mining
• classification
• clustering
• machine learning

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Samia Noureddine

Samia Noureddine obtained her state engineering degree in electronics in 1996 at Mohamed Khider University of Biskra. She had her master's degree in electronics in June 2018 at the same university. She is preparing a doctoral thesis at LESIA Laboratory, her research fields are data mining, predictive decision support and smart health.

Baarir Zineeddine

Prof. Baarir Zineeddine received his Bachelor of Eng in Electrical & Electronic Engineering from Swansea University (U.K) in 1981 and his P.hd in Signal Processing from Orsay University, Paris XI (France) in 1986. He is a currently Professor in Signal & Image Processing at Mohamed Khider University of Biskra (Algeria). He is a research Director at LESIA Laboratory in Image Processing.

Abida Toumi

Dr. Abida Toumi received her Bachelor of Eng in Electrical & Electronic Engineering from Mohamed Khider University centre of Biskra (Algeria) in 1995, her Magister in Electrical & Electronic Engineering from Farhat ABASS University of Sétif (Algeria) in 2002 and her grade of Doctor of Science in Electrical & Electronic Engineering from Mohamed Khider University of Biskra (Algeria). She is a research professor at LESIA Laboratory in Methaheuristics, Systems and Image Processing.

Abir Betka

Dr. Abir Betka, received the master degree in signal and communication from electrical engineering department at Mohamed Khider University of Biskra, Algeria in 2015, and her PhD degree in signal and communication from the same university in 2019. Her research interests include Image processing, Electrical engineering, Optimization, Metaheuristics, Nature inspired techniques.

Aïcha-Nabila Benharkat

Aïcha-Nabila Benharkat is Associate Professor at the Computer Science Department (INSA de LYON) since 1992.In this role, she worked on the Integration of heterogeneous databases using Description Logics. Her research activities are in line with the continuity of the topic of interoperability in information systems (2001 to 2010), ieschema matching techniques in small and large scale, business process as well as quality, Web services discovery and evolution of WEB services in SOA architectures. Since 2013, they have evolved into issues of cloud application development and, more recently, since 2016, towards the issue of the management of privacy during the life cycle of data from connected objects.