An Analytical Evaluating Strategy for Feature Correlation based Disease Prediction in Health Care Data mining

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1 An Analytcal Evaluatng Strategy for Feature Correlaton based Dsease Predcton n Health Care Data nng Jadhav Anal M.Tech n Inforaton Technology Sreendh Insttute of Scence and Technology Abstract Due to the senstvty of the nforaton requred to treat a dsease of a patent effcently, the healthcare ndustry collects huge aounts of healthcare data, the volue and the detals of ths data ncreased n recent years, whch s due to the patent nforaton acqurng strateges called Electronc Health Records (EHR) and Electronc Medcal Records (EMR). As Data nng s vtal and portant n an exploratory analyss because of nontrval nforaton n large volues of data, t plays an portant and key role n health care ndustry. In partcular data nng s havng vtal scope n health care ndustry needs such as dsease predcton, estaton of scenaros that leads to possblty of a dsease. In ths regard we explored a statstcal nng odel called dsease predcton by degree of dsease possblty, whch s aed to estate the degree of dsease possblty threshold fro gven electronc edcal records for tranng. Wth the otvaton ganed fro our odel that explored n our earler work, here we devsed a novel and proved nng strategy for dsease predcton. In ths nng strategy a feature correlaton s consdered to estate the degree of dsease possblty. The experental results ndcatng that the feature correlaton s havng sgnfcant pact towards dsease predcton by degree of dsease possblty. Keywords: Health Mnng, Assocablty, Dsease predcton, HITS algorth, weghted assocatve classfers. Ms.N.Dvya Assstant Professor Sreendh Insttute of Scence and Technology 1.INTRODUCTION Data nng s the dentfcaton of forerly undentfed, plct nforaton or facts havng edate plcatons fro enorous quanttes of raw data or large volues of databases [1]. It s a process of utual nteracton of huan and coputers and a strategc balance of the huan sklls defnng the probles and goals cobned wth the coputers search and analyss abltes to produce effectve outcoes. Data nng s useful for exanng routnely collected large aounts of data. It s ost useful for exploratory analyss of large volues of data contanng nontrval nforaton. The obectves of data nng are predcton and descrpton. Predcton s used to fnd unfalar or undentfed varables applyng known varables on the data set and Descrpton prarly gves nsghts nto actual, expected and obvous detals n a falar way for huans to understand. Data nng perfors a crucal role n Dsease Predcton. The technque helps n forecastng several dseases lke Hepatts, Lung cancer, Lver dsorder, and Breast cancer, Thyrod dsease, Dabetes and any ore. Patent s data recorded by edcal professonals s very useful to fnd nforaton that s relevant and s of edate applcaton. Technques such as statstcal analyss and data nng, studed wdely by researchers today, are prarly focused on helpng edcal professonals n predctng dseases especally heart related dseases. Several such analyses categorze heart dsease by factors such as age, obesty, lack of physcal actvty [2], sokng habt [3], blood pressure, hypertenson, total cholesterol [4], dabetes [5], and faly hstory [6]. The crtera help edcal professonals n gvng Page 1619

2 tely dagnoss and treatent to patents wth hgh rsk factors. Decson tree, naïve bayes, neural network, baggng, kernel densty and support vectl or achne are soe of the statstcal technques beng nvestgated by researchers on varous heart dsease datasets [7]-[12]. They have showed n [13] correct classfed accuracy of approxately 77% wth logstc regresson and coparatvely ore effectve accuracy s observed by applyng odel R-C4.5 based on C4.5. Rules generated for ths odel C4.5 provdes edcal experts wth better nsghts nto the dsease [14]. The coon nterest of all of these sad odels s the feature selecton and feature optzaton towards scalable predctons. Hence here n ths paper, we devsed a statstcal nng odel to predct the degree dsease possblty usng the correlaton of the features extracted fro gven patent record, whch s an extenson to our earler odel [15]. 2.RELATED WORK Jyot Son et. al [16] ntroduced three types of supervsed achne learnng algorths for studyng the heart probles dataset [17]. The data s classfed usng Tanagra data nng tool evaluated wth 10 fold cross valdaton and coparng the outcoes. The algorths proposed are Decson Lst algorth, Nave Bayes and K - NN. The algorths detals are as below; )Decson tree requres sple steps for ntaton, handles large aount of densonal nforaton, s perfect for exploratory knowledge dscovery and fnally the results ganed fro Decson Tree are easer to read and nterpret [1] resultng t to be used wdely. )Nave Bayes s a statstcal classfer where the characterstcs have no dependency assgned to the, requres posteror probablty to be axzed for dentfyng the group and does not requre any Bayesan approach for executon. The effcency n executon s hgh and the algorth s sple copared to all other achne learnng algorths. ) k-nn algorth has loud features that hghly reduces the perforance and ths concluson s reached after tranng and executng 3000 nstances wth 14 dfferent attrbutes. Jyot Son et a; [16] addtonally proposed connecton rule data-nng technque for predctng one s heart dsease. However they ntroduced vared rules n large nuber for organzaton gudelnes used over health related datasets, ost of the clncally neglgble for the data. Subsequently to decrease the nuber of gudelnes the authors pleented four restrctons.e., te flterng, feature group, optu product set sze as well as antecedent/consequent prncple restrctons. Another proble was wthout relevance the organzaton rules are applcable to the entre dataset and to overcoe these ltatons and to reduce the large nuber of gudelnes another algorth was ntroduced. In the new algorth the verfcaton of the valdaton outcoe s done wth set queres, organzaton gudelnes and evaluaton set. A new paraeter lft s appled n place of help and assurance and another paraeter Rase s used for easurng and assessng the dependablty and edcal portance of organzaton gudelnes. The valdaton of the results by physcans s defned by susceptblty and specfcty. The accuracy of deternng hgh rsk patents based on chance and susceptblty s further defned by the correct deternaton of a healthy ndvdual based on unqueness. There are three steps n the forula for deternng the predctve assocaton rules n health-related dataset [18] where; () Converson of edcal data-set coprsng of nuerc and categorcal attrbutes nto trade data-set should be done. () Predctve assocaton gudelnes wth edcally vald features should be obtaned by Page 1620

3 ncorporatng all prevously revewed four constrants nto the search process. () Test and tran strategy for valdatng the organzaton rules ought to be utlzed. Genetc algorths reduce [19] the orgnal sze of data to a ost effectve sub-set of suffcent sze, enough for forecastng heart dsease. Classfcaton s a strategy of supervsed learnng to odel datasets of vald defnton.the ost portant role of feature selecton s to nze the densonalty and prove classfcaton. Data nng n edcal doan s not the sae as n that of other doans due to ultple attrbutes. Generally predctng a dsease nvolves dagnoss wth ultple tests that usually ncur huge costs, data nng technques n contrast analyzes specfc attrbutes of the dsease and forecasts the dsease probablty and elnates the requreent of ost of the tests and ts expenses. Here n healthcare doan the attrbutes are any and the approach of densonalty reducton s a aor strategy for nzng the nuber of attrbutes and accurately predctng the dseases. The feature correlaton based heart rate varablty was checked n [21], whch was done by a classfcaton approach that supported by statstcal analyss on correlated categorcal features. The experents explored n [21] shown that the usage of support vector achne (SVM) as a classfer delvered accuracy n classfcaton and operatonal process. The heart dsease predcton usng classfers such as Decson Trees, Navy Byes and Nearest Neghbor were explored n [22]. The results llustrated that the classfcaton accuracy s proportonate to feature optzaton. In ths regard we devsed a nng statstcal nng ethod n our earler work [15], whch s n the a of predctng the scope of the dsease by degree of dsease possblty. Ths odel s estatng the pact of the each feature as ndvdual towards dsease possblty, but n vgorous experental analyss, the consstency of the odel s volated as ore than one feature together reflectng dvergence n estatng the degree of possblty, whch s due to ther categorcal values. Henceforth here n ths paper we explored a novel odel that attepts to estate the pact of ultple features together to explore the degree of dsease possblty. 3.Dsease Predcton by Estatng Feature Correlaton Towards Degree of Dsease Possblty A etrc called closeness support was devsed n our earler odel [15], whch s aed to estate the pact of each feature towards degree of dsease possblty. In ths regard we consdered a set of edcal records of dvergent heart dsease patents and set of features representng the values n those edcal records. Further a bpartte graph was buld between features and edcal records, and then a refned HITS algorth was appled to estate the wats of the features and records. Further these values were used to easure the degree of dsease possblty. Experental results on benchark dataset also ndcated that the odel s effectve towards predctng the dsease possblty. But the experental results on ncreasng set of edcal records ndcatng that often the dsease possblty estaton s contradct n regard to ore than one feature as a set, whch s due to the categorcal values of these features. Henceforth here we refned the sad odel such that the correlated feature-set wll be taken as one unt rather than an ndvdual feature to estate the degree of dsease possblty threshold. In ths DDP-FC proposal a bpartte graph s buld between edcal records and set of correlated features. 3.1Estatng Correlaton between Features: Pearson correlaton coeffcent [20] and ean-square contngency coeffcent [20] are two bench ark odels to assess the correlaton between any two attrbutes wth contnuous and categorcal values respectvely. As descrbed n our earler work [15] the attrbutes taken n to account of dsease predcton are categorcal. Henceforth here we use ean-square contngency coeffcent [20] to estate the correlaton between attrbutes. A and B such thata,a,a,..a, are categorcal values of A and B respectvely. The Page 1621

4 sze of the set of values appeared for A s and B s n. Then the ean square contngency coeffcent between attrbutes A and B can be easured as follows: n 1 1..(Eq1) ( a, b ) 1 1 Here n ths equaton Eq1, occurrence of a, b (Eq2) ( a ) Here n ths equaton Eq2, occurrence of a n (Eq3) ( b ) Here n ths equaton Eq3, occurrence of b 2 ( AB) 1 1 s the fracton of co s the fracton of s the fracton of n 1 (. ) *.. (Eq4) n( n, ) 1. Here n ths equaton Eq4, 2 s the ean square ( A B) contngency coeffcent that ndcates the correlaton between attrbutes A and B.Snce the attrbutes used n dsease predcton are ostly contans values that are categorcal, henceforth k-edod clusterng technque can be used to group the attrbutes based on ther correlaton. 3.2Degree of Dsease Possblty by Feature Correlaton (DDP-FC) A.Assuptons: Let set of edcal records r1, r2, r3,... rn Fored by the set of features (Eq5) 2 fc FS f FS. 1 Here n the above equaton feature set FS contans all features { f1, f2,... f n } that are used to for each edcal record. Let SCRFS as set of correlated feature sets such that each correlated feature set { crfscrfs SCFRS} The property of a correlated feature set s crfs 1 { f crfscrfs FS}. (Eq6) Here n above descrpton SCRFS represents the set of correlated feature sets found by usng the approach descrbed n secton 3.1. crfs s a correlated feature set whch s belongs to SCRFS and t represents set correlated features such that these features are subset of FS. Process In the process of detectng the closeness of each correlated feature set wth edcal records, ntally we buld a b-parted graph between edcal records and all correlated feature sets. Fg 1: bpartte graph between edcal records and correlated feature sets If an edge found between a edcal record r and a correlaton feature set crfs then the weght of that edge ew can be found as follows ew ( r crfs ) r crfs. (Eq7) r ( r crfs ) Page 1622

5 Let consder a set of edcal records MR MR r MR as a database such that each 1 edcal record r contans one or ore features that belongs to feature set FS. Then the edcal records MR can be represented as bpartte graph G as G ( MR, FS, E) Here E {( r, crfs) crfs SCRFS, crfs r, r MR} The graph representaton (see fg 1) of the set of edcal records gves us the dea of applyng lnkbased rankng odels for the evaluaton of connected sets. In ths bpartte graph, the closeness support of a edcal record s proportonal to degree of all ts correlated feature sets weght. However, t s crucal to have dfferent closeness weghts for dfferent edcal records n order to reflect ther dfferent portance. The evaluaton of edcal record nfluence should be derved fro these weghts. Here coes the queston of how to acqure weghts n a set of edcal records. Intutvely, a edcal record wth hgh closeness weghts should contan any of the correlaton feature sets those belongs to the sae edcal record wth hgh closeness support; at the sae te, a edcal record wth hgh closeness support should be contaned by less or zero other edcal records wth hgh closeness weghts. The renforcng relatonshp of edcal records and correlaton feature sets s ust lke the relatonshp between hubs and authortes n the HITS odel [23]. Table 2: Transpose atrx of atrx as fallows that represents the connecton between a case and each edcal record. Regardng, the edcal records as "pure" hubs and the correlaton feature sets as "pure" authortes, we can apply HITS to ths bpartte graph. The followng explored the process: Table 1: atrx A as follows that represents the connecton weghts between a feature and each edcal record r Let atrx representaton of edcal records and correlated feature sets as a atrx 'A'(see Table 1). The value represents the edge weght between edcal record and correlaton feature set that calculated by usng Eq7. Page 1623

6 Consder the atrx that representng each hub ntal value as 1 (see fg 2). Fg 2: Intally consder the each hub weght as 1 by default as fallow and represent the as atrx u. Here n the above equaton MR ndcates the total nuber of edcal records The degree of dsease possblty range can be explored as follows Lower threshold of ddpt range s Transpose the atrx A as A (see Table 2) Fnd Authorty weghts by ultplyng A' wth u as v A' u (Matrx ultplcaton between A and u gves a atrx v that representng the authorty weghts) Now fnd the orgnal hub weghts through atrx ultplcaton between Aand v. u A v Then the Cs of correlaton feature set crfs can be easured as follows cs( crfs) 1 ddp( r ) 1 { u( r ) : ( crfs r ) 0} 1 1 u( r ) { cs( crfs ) : ( crfs r ) 0} ec( r ) Here n the above equaton ec( r ) represents the total nuber of edges connected to edcal record r Then the degree of dsease possblty threshold can be found as follows: ddpt MR 1 ddp( r ) MR ddpt MR dv( r ) 1 l ddpt MR Here n ths equaton dv( r ) ndcates the devaton of the ddp value of edcal record r Hgher threshold of ddpt range s ddpt MR dv( r ) 1 h ddpt MR Medcal record r can be sad as safe f and only f ddp( r) ddptl Medcal record r can be sad as dsease possblty rsk s hgh f and only f ddp( r) ddpt && ddp( r) ddpt l Medcal record r can be confred as effected by dsease f ddp( r) ddpth 4.EMPIRICAL ANALYSIS OF THE PROPOSED MODEL We explored the credblty of the proposed odel on heart dsease dataset. The above sad data set contans 5136 saples, out of that 4136 saples were used to devse the degree of dsease possblty threshold and ts upper and lower bounds. Further we used the rest 1000 records to predct the dsease scope. Interestngly, the eprcal h Page 1624

7 study delvered prosng results. The statstcs explored n Table 3 Table 3: Statstcs of the experent results Total Nuber of Records 5136 Range of felds count n a 14 record Total nuber of Features 174 Found n dataset Total nuber of correlaton 24 sets found Total nuber of bpartte edges found Degree of Dsease possblty threshold found: Degree of Dsease possblty threshold Upper Bound Degree of Dsease possblty threshold Lower Bound Total records Tested 1000 Total nuber of records found wth DDP less than lower bound 23 (false negatve 3 and true negatve 20) Total nuber of records found wth DDP greater than lower bound 977 (true postves 956 and false postves 21) As per the results explored n Table 3, the proposed odel s accurate to the level of 81.7%. The falure percentage s 18.3%, whch s nonal. The experents also conducted on the sae data set wth earler ethod whch s not consderng the correlaton factor of the features, and the results are as follows: Total records Tested 1000 Total nuber of records found wth DDP less than lower bound 292 (false negatve 205, true negatves 87) Total nuber of records found wth DDP greater than lower bound 708 (false postves 134 true postves 574) As per these results, the accuracy of the degree of dsease possblty wthout correlaton feature set factor s less sgnfcant snce we observed that the predcton success lted to 70.0%. The falure percentage s approx 30%, whch s not a neglgble factor. Hence t s obvous to conclude that the correlaton feature sets are ore sgnfcant copared to ndependent features towards to easure the degree of dsease possblty A. Perforance Analyss We used dsease predcton accuracy (the percentage of vald predctons by the proposed) as the an perforance easure. In addton to easurng accuracy, the precson, recall, and F-easure were used to easure the perforance; these are defned usng followng equatons. t pr t f Here n above Equaton the pr ndcates the precson, t ndcates the true postves and f ndcates the false postve t rc t f Here n above Equaton, the rc ndcates the recall, f ndcates the false negatve. F 2* pr * rc pr rc Here n the above Equaton, F ndcates the F- easure. Table 4: Precson, recall and F-easure values found fro the results of the eprcal analyss. Precson recall f-easure DDP DDP-FC Page 1625

8 Fg 3: perforance analyss of the estatng Degree of Dsease Possblty wth and wthout Correlaton analyss of the features. CONCLUSION: Correlaton analyss of the features and clusterng the, whch s based on ther ean square contngency correlaton coeffcent, s a sgnfcant approach towards provng the accuracy of easurng degree of dsease possblty. The vgorous experents wth dvergent sze of the edcal records, t s observed that our earler nng approach [15] that easures degree of dsease possblty threshold s not sgnfcant aganst dvergence n feature values. Henceforth here n ths paper we devsed a feature correlaton analyss by ean square contngency threshold, whch s used further to cluster these feature by k-edod approach. Then these clusters wth hghly correlated features are used along wth edcal records to buld a bpartte graph. The experental results ndcatng that the odel devsed here n ths paper s sgnfcantly proved the accuracy of easurng degree of dsease possblty threshold copared to our earler odel [15]. In future the cluster refneent and optzaton can be devsed, whch s necessary n stuaton f axal nuber of clusters found due to broad range of categorcal values of the features. REFERENCES [1] Han, J., Kaber, M.: Data Mnng Concepts and Technques, Morgan Kaufann Publshers, 2006 [2] Data nng: Introductory and Advanced Topcs Margaret H. Dunha [3] Jyot Son, Ua Ansar, Dpesh Shara, Sunta Son Predctve Data Mnng for Medcal Dagnoss: An Overvew of Heart Dsease Predcton IJCSE Vol. 3 NO. 6 June 2011 [4]Carloz Ordonez, Assocaton Rule Dsco very wth Tran and Test approach for heart dsease predcton, IEEE Transactons on Inforaton Technology n Boedcne, Volue 10, No. 2, Aprl 2006.pp [5]M. ANBARASI, E. ANUPRIYA, N.CH.S.N. IYENGAR, Enhanced Predcton of Heart Dsease wth Feature Subset Selecton usng Genetc Algorth, Internatonal Journal of Engneerng Scence and Technology Vol. 2(10), 2010, [6]G. Parthban, A. Raesh, S.K.Srvatsa Dagn oss of Heart Dsease for Dabetc Patents usng Nave Bayes Method [7]Cho J.P., Han T.H. and Park R.W., A Hybrd Bayesan Network Model for Predctng Breast Cancer Prognoss, J Korean Soc Med Infor, 2009, pp [8]Bellaacha Abdelghan and Erhan Guven, "Predctng Breast Cancer Survvablty usng Data Mnng Technques,"Nnth Workshop on Mnng Scentfc and Engneerng Datasets n conuncton wth the Sxth SIAM Internatonal Conference on Data Mnng, [9]Lundn M., Lundn J., BurkeB.H.,Tokkanen S., Pylkkanen L. and Joensuu H., Artfcal Neural Networks Appled to Survval Predcton n Breast Cancer, Oncology Internatonal Journal for Cancer Resaerch and Treatent, vol. 57, [10]Delen Dursun, Walker Glenn and Kada At, Predctng breast cancer survvablty: a coparson of three data nng ethods, Artfcal Intellgence n Medcne,vol. 34, pp , June [11]Ruben D. Canlas Jr., Data Mnng In Healthcare: Current Applcatons And Issues, August 2009 [12]Mchael Feld, Dr. Mchael Kpp, Dr. Alassane Ndaye and Dr. Donk Heckann Weka: Practcal achne learnng tools and technques wth Java pleentatons. Page 1626

9 [13]Jon M. Klenberg Authortatve Sources n a Hyperlnked Envronent ell.edu/hoe/klenber/auth.pdf [14]K.P Soan, Shya Dwakar, V.Vay Insght nto Data nng theory and practce [15]Asha Rakuar, G.Sopha Reena, Dagnoss Of Heart Dsease Usng Datanng Algorth, Global Journal of Coputer Scence and Technology 38 Vol. 10 Issue 10 Ver. 1.0 Septeber [16]Shantakuar B.Patl, Y.S.Kuara sway, Intellgent and Effectve Heart Attack Predcton Syste Usng Data Mnng and Artfcal Neural Network, European Journal of Scentfc Research ISSN X Vol.31 No.4 (2009), pp EuroJournals Publshng, Inc [17]Wngo PA, Tong T, Bolden S, Cancer statstcs, 1995, CA Cancer J Cln 45 (1995), no. 1,8-30. [18]Fentan IS, Detecton and treatent of breast cancer, London: Martn Duntz (1998). [19] dsease/reprocessed.hungaran.data [20]Raana.N, Dr.C.V.Guru Rao "Conte porary Affraton of the Recent Lterature on Dsease Predcton Usng Data Mnng Technques"; Internatonal Journal of Scentfc & Engneerng Research, Volue 4, Issue 6, June 2013 ISSN Page 1627