{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:29:44Z","timestamp":1777703384582,"version":"3.51.4"},"reference-count":26,"publisher":"SAGE Publications","issue":"3","license":[{"start":{"date-parts":[[2018,3,22]],"date-time":"2018-03-22T00:00:00Z","timestamp":1521676800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"published-print":{"date-parts":[[2018,3,22]]},"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3233\/jifs-169455","type":"journal-article","created":{"date-parts":[[2018,3,23]],"date-time":"2018-03-23T12:25:31Z","timestamp":1521807931000},"page":"1609-1618","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":19,"title":["A multimodal adaptive approach on soft set based diagnostic risk prediction system"],"prefix":"10.1177","volume":"34","author":[{"given":"Terry Jacob","family":"Mathew","sequence":"first","affiliation":[{"name":"School of Computer Sciences, Mahatma Gandhi University, Kottayam, Kerala, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Elizabeth","family":"Sherly","sequence":"additional","affiliation":[{"name":"IIITM-K, Technopark, Trivandrum, Kerala, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jos\u00e9 Carlos R.","family":"Alcantud","sequence":"additional","affiliation":[{"name":"BORDA Research Unit and Multidisciplinary Institute of Enterprise (IME), University of Salamanca, Salamanca, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2018,3,22]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2015.08.007"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2017.06.010"},{"key":"e_1_3_2_4_2","first-page":"49","volume-title":"Glaucoma diagnosis: A soft set based decision making procedure","author":"Alcantud J.C.R.","year":"2015","unstructured":"AlcantudJ.C.R., Santos-Garc\u00edaG. and Hern\u00e1ndez-GalileaE., Glaucoma diagnosis: A soft set based decision making procedure. In Conference of the Spanish Association for Artificial Intelligence (2015) 49\u201360. Springer."},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2011.01.003"},{"issue":"1","key":"e_1_3_2_6_2","first-page":"2013","article-title":"Fuzzy soft set theory applied to medical diagnosis using fuzzy arithmetic operations","volume":"82","author":"\u00e7elik Y.","year":"2013","unstructured":"\u00e7elikY., and YamakS., Fuzzy soft set theory applied to medical diagnosis using fuzzy arithmetic operations, Journal of Inequalities and Applications82(1) (2013), 2013.","journal-title":"Journal of Inequalities and Applications"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2014.06.050"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0165-0114(98)00235-8"},{"issue":"1","key":"e_1_3_2_9_2","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.fiae.2017.03.004","article-title":"Bell-shaped fuzzy soft sets and their application in medical diagnosis","volume":"9","author":"Dutta P.","year":"2017","unstructured":"DuttaP. and LimbooB., Bell-shaped fuzzy soft sets and their application in medical diagnosis, Fuzzy Information and Engineering9(1) (2017), 67\u201391. ISSN 1616-8658.","journal-title":"Fuzzy Information and Engineering"},{"key":"e_1_3_2_10_2","first-page":"1","article-title":"Probabilistic soft sets and dual probabilistic soft sets in decision-making","author":"Fatimah F.","year":"2017","unstructured":"FatimahF., RosadiD., HakimR.F. and AlcantudJ.C.R., Probabilistic soft sets and dual probabilistic soft sets in decision-making, Neural Computing and Applications (2017), 1\u201311.","journal-title":"Neural Computing and Applications"},{"issue":"1","key":"e_1_3_2_11_2","first-page":"69","article-title":"Soft rough sets applied to multicriteria group decision making","volume":"2","author":"Feng F.","year":"2011","unstructured":"FengF., Soft rough sets applied to multicriteria group decision making, Annals of Fuzzy Mathematics and Informatics2(1) (2011), 69\u201380. ISSN 2093-9310.","journal-title":"Annals of Fuzzy Mathematics and Informatics"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-009-0465-6"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2010.11.004"},{"key":"e_1_3_2_14_2","first-page":"529","volume-title":"Application of wavelet de-noising filters in mammogram images classification using fuzzy soft set","author":"Lashari S.A.","year":"2016","unstructured":"LashariS.A., IbrahimR., SenanN., YantoI.T.R. and HerawanT., Application of wavelet de-noising filters in mammogram images classification using fuzzy soft set, In International Conference on Soft Computing and Data Mining (2016) 529\u2013537. Springer."},{"issue":"4","key":"e_1_3_2_15_2","first-page":"507","article-title":"A survey of decision making methodsbased on certain hybrid soft set models","volume":"47","author":"Ma X.","year":"2017","unstructured":"MaX., LiuQ. and ZhanJ., A survey of decision making methodsbased on certain hybrid soft set models, Artificial IntelligenceReview47(4) (2017), 507\u2013530.","journal-title":"Artificial IntelligenceReview"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.3233\/JIFS-161681"},{"key":"e_1_3_2_17_2","first-page":"589","article-title":"Fuzzy soft sets","volume":"9","author":"Maji P.","year":"2001","unstructured":"MajiP., BiswasR. and RoyA., Fuzzy soft sets, Journal of Fuzzy Mathematics9 (2001), 589\u2013602.","journal-title":"Journal of Fuzzy Mathematics"},{"key":"e_1_3_2_18_2","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/S0898-1221(99)00056-5","article-title":"Soft set theory - first results","volume":"37","author":"Molodtsov D.","year":"1999","unstructured":"MolodtsovD., Soft set theory - first results, Computers and Mathematics with Applications37 (1999), 19\u201331.","journal-title":"Computers and Mathematics with Applications"},{"key":"e_1_3_2_19_2","first-page":"246","volume-title":"Texture classification using a novel, soft-set theory based classification algorithm","author":"Mushrif M.M.","year":"2006","unstructured":"MushrifM.M., SenguptaS. and RayA.K., Texture classification using a novel, soft-set theory based classification algorithm, In Asian Conference on Computer Vision (2006) 246\u2013254. Springer."},{"key":"e_1_3_2_20_2","article-title":"Algorithms for interval-valued fuzzy soft sets in stochastic multi-criteria decision making based on regret theory and prospect theory with combined weight, pages forthcoming","author":"Peng X.","year":"2016","unstructured":"PengX. and YangY., Algorithms for interval-valued fuzzy soft sets in stochastic multi-criteria decision making based on regret theory and prospect theory with combined weight, pages forthcoming, Applied Soft Computing, 2016.","journal-title":"Applied Soft Computing"},{"issue":"1","key":"e_1_3_2_21_2","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/0165-0114(79)90017-4","article-title":"Inverses of fuzzy relations. application to possibility distributions and medical diagnosis","volume":"2","author":"Sanchez E.","year":"1979","unstructured":"SanchezE., Inverses of fuzzy relations. application to possibility distributions and medical diagnosis, Fuzzy Sets and Systems2(1) (1979), 75\u201386.","journal-title":"Fuzzy Sets and Systems"},{"issue":"1","key":"e_1_3_2_22_2","first-page":"1","article-title":"A fuzzy approach for determination of prostate cancer","volume":"1","author":"Saritas I.","year":"2013","unstructured":"SaritasI., AllahverdiN. and SertI.U., A fuzzy approach for determination of prostate cancer, International Journal of Intelligent Systems and Applications in Engineering1(1) (2013), 1\u20137.","journal-title":"International Journal of Intelligent Systems and Applications in Engineering"},{"key":"e_1_3_2_23_2","volume-title":"Computer-based medical consultations: MYCIN","author":"Shortliffe E.","year":"2012","unstructured":"ShortliffeE., Computer-based medical consultations: MYCINvol. 2. Elsevier, 2012."},{"key":"e_1_3_2_24_2","first-page":"131","volume-title":"Computerized classification of malignant and normal microcalcifications on mammograms: Using soft set theory","author":"Sreedevi S.","year":"2016","unstructured":"SreedeviS., MathewT.J. and SherlyE., Computerized classification of malignant and normal microcalcifications on mammograms: Using soft set theory. In Information Science (ICIS), International Conference on> 131\u2013137. IEEE, 2016."},{"key":"e_1_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.1186\/1029-242X-2013-229"},{"key":"e_1_3_2_26_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0019-9958(65)90241-X"},{"key":"e_1_3_2_27_2","doi-asserted-by":"publisher","DOI":"10.3233\/IFS-151732"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-169455","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.3233\/JIFS-169455","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-169455","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:38:35Z","timestamp":1777455515000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.3233\/JIFS-169455"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,3,22]]},"references-count":26,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2018,3,22]]}},"alternative-id":["10.3233\/JIFS-169455"],"URL":"https:\/\/doi.org\/10.3233\/jifs-169455","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,3,22]]}}}