Neutrosophic recommender system for medical diagnosis based on algebraic similarity measure and clustering

In this paper, we propose a neutrosophic recommender system for medical diagnosis using both neutrosophic similarity measure and neutrosophic clustering to capture the treatment of similar patients at different levels within a concurrent group. The proposed algorithm allows similar patients being treated concurrently in a group. Firstly, the similarities are measured based on the algebraic operations and their theoretic properties. Secondly, a clustering algorithm is used to identify neighbors that are in the same cluster and share common characteristics. Then, a prediction formula using results of both the clustering algorithm and the similarity measures is designed. Experiment indicates the advantages and superiority of the proposal.

[1]  Le Hoang Son,et al.  A Neutrosophic Recommender System for Medical Diagnosis Based on Algebraic Neutrosophic Measures , 2016, Appl. Soft Comput..

[2]  E. Kazerooni,et al.  Automated iterative neutrosophic lung segmentation for image analysis in thoracic computed tomography. , 2013, Medical physics.

[3]  Surapati Pramanik,et al.  COTANGENT SIMILARITY MEASURE OF ROUGH NEUTROSOPHIC SETS AND ITS APPLICATION TO MEDICAL DIAGNOSIS , 2015 .

[4]  Darcy A. Davis,et al.  Predicting individual disease risk based on medical history , 2008, CIKM '08.

[5]  Nilanjan Dey,et al.  Decision Making Based on Fuzzy Aggregation Operators for Medical Diagnosis from Dental X-ray images , 2016, Journal of Medical Systems.

[6]  William Nick Street,et al.  Healthcare information systems: data mining methods in the creation of a clinical recommender system , 2011, Enterp. Inf. Syst..

[7]  Jae Kyeong Kim,et al.  A literature review and classification of recommender systems research , 2012, Expert Syst. Appl..

[8]  Jun Ye,et al.  Improved cosine similarity measures of simplified neutrosophic sets for medical diagnoses , 2015, Artif. Intell. Medicine.

[9]  F. Smarandache.  A Unifying Field in Logics: Neutrosophic Logic. , 1999 .

[10]  Pachaiyappan Muthukumar,et al.  A similarity measure of intuitionistic fuzzy soft sets and its application in medical diagnosis , 2016, Appl. Soft Comput..

[11]  Jing Fu,et al.  Multi-period medical diagnosis method using a single valued neutrosophic similarity measure based on tangent function , 2015, Comput. Methods Programs Biomed..

[12]  Le Hoang Son.  Enhancing clustering quality of geo-demographic analysis using context fuzzy clustering type-2 and particle swarm optimization , 2014, Appl. Soft Comput..

[13]  Lior Rokach,et al.  Introduction to Recommender Systems Handbook , 2011, Recommender Systems Handbook.

[14]  Mumtaz Ali,et al.  A Novel Clustering Algorithm in a Neutrosophic Recommender System for Medical Diagnosis , 2017, Cognitive Computation.

[15]  Surapati Pramanik.  Cosine Similarity Measure Of Rough Neutrosophic Sets And Its Application In Medical Diagnosis , 2015 .

[16]  Irfan Deli,et al.  Correlation Measure for Neutrosophic Refined Sets and its application in Medical Diagnosis , 2013 .

[17]  Heng-Da Cheng,et al.  Segmentation of ultrasound breast images based on a neutrosophic method , 2010 .

[18]  Pei-Chann Chang,et al.  A hybrid model combining case-based reasoning and fuzzy decision tree for medical data classification , 2011, Appl. Soft Comput..

[19]  Jun Ye,et al.  Dice Similarity Measure between Single Valued Neutrosophic Multisets and Its Application in Medical Diagnosis , 2015 .

[20]  Saeid Nahavandi,et al.  Medical data classification using interval type-2 fuzzy logic system and wavelets , 2015, Appl. Soft Comput..

[21]  Zeeshan Syed,et al.  From netflix to heart attacks: collaborative filtering in medical datasets , 2010, IHI.

[22]  Jun Ye,et al.  Medical Diagnosis Using Distance-Based Similarity Measures of Single Valued Neutrosophic Multisets , 2015 .