Journal of Intelligent & Fuzzy Systems 34 (2018) 1609–1618 DOI:10.3233/JIFS-169455 IOS Press 1609 A multimodal adaptive approach on soft set based diagnostic risk prediction system Terry Jacob Mathew a,∗ , Elizabeth Sherly b and Jos´ e Carlos R. Alcantud c a School of Computer Sciences, Mahatma Gandhi University, Kottayam, Kerala, India b IIITM-K, Technopark, Trivandrum, Kerala, India c BORDA Research Unit and Multidisciplinary Institute of Enterprise (IME), University of Salamanca, Salamanca, Spain Abstract. The diagnostic prediction models in medical sciences are more relevant today than ever before. The nature and type of the data do have a profound impact on the prediction output. As the nature of data changes, the choice of intelligent methods also has to be altered adaptively to attain promising results. A highly customised data oriented model which encompasses multi-dimensional information can aid and improve the prediction process. This paper proposes an adaptive soft set based intelligent system which is designed to receive a set of input parameters related to any disease and generates the risk percentage of the patient. The system produces soft sets with the given inputs by fuzzification; followed by rule generation. The rules are analysed to obtain the risk percentage and based on its intensity, the system proceeds with the disease diagnosis. Four different approaches are introduced in this study to enhance the risk prediction accuracy, namely subset of parameters method, adaptive selection of analysis metrics, weighted rules method and the unique set method. The best model is acquired from these approaches in an adaptive fashion by the algorithm. Our method of risk prediction is applied for prostate cancer detection as a case study and we provide exhaustive comparison of the different approaches employed within the algorithm. The results prove that this synergistic approach gives better prediction results than the existing methods. The combination of unique set and weighted approach gave the best predictive solution for the proposed system. Keywords: Soft set, fuzzy set, adaptive decision making, risk prediction 1. Introduction Artificial intelligence, often described as the sci- ence and engineering of making intelligent machines, broadly indicates the use of a computer to model intelligent behaviour with minimal human interven- tion. It is applied in a wide range of domains in medicine such as robotics, medical diagnosis, medi- cal statistics and human physiology. The applications arising out of these include robotic assistance for elderly patients, predictive solutions for surgeons, use of nano robots in treatment regimen etc. In spite ∗ Corresponding author. Terry Jacob Mathew, School of Com- puter Sciences, Mahatma Gandhi University, Kottayam, Kerala, India. E-mail: terryjacobin@gmail.com. of these, researchers have noted that the difficulty in diagnosis is due to the vast amount of medical data, similarity of symptoms for many diseases and lack of diagnostic expertise among physicians. These reasons encourage researchers to design more accu- rate intelligent tools and techniques to diagnose a disease with acceptable performance, which benefit from savings in terms of time, money and manpower. Computer aided decision making (CAD) has widely developed its applications in solving real world problems, especially in medical diagnosis, as the precision of medical decisions can be enhanced with intelligent methods. The presence of intelli- gent systems in the field of medical sciences has influenced medical diagnosis to a great extent with notable examples such as MYCIN [6, 22], etc. This 1064-1246/18/$35.00 © 2018 – IOS Press and the authors. All rights reserved