Affiliations: [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
Correspondence:
[*]
Corresponding author. Terry Jacob Mathew, School of Computer Sciences, Mahatma Gandhi University, Kottayam, Kerala, India. E-mail: [email protected].
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.
