Machine Learning Algorithm Improves Accuracy for analysing Kidney Function Test Using Decision Tree Algorithm

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1 Internatonal Journal of Management, IT & Engneerng Vol. 8 Issue 8, August 2018, ISSN: Impact Factor: Journal Homepage: Double-Blnd Peer Revewed Refereed Open Access Internatonal Journal - Included n the Internatonal Seral Drectores Indexed & Lsted at: Ulrch's Perodcals Drectory, U.S.A., Open J-Gage as well as n Cabell s Drectores of Publshng Opportuntes, U.S.A Machne Learnng Algorthm Improves Accuracy for analysng Kdney Functon Test Usng Decson Tree Algorthm Dr. M. Maylvaganan * R.Rajaman ** Keywords: C4.5 Algorthm wth Bootstrap Aggregaton (Baggng) Algorthm; Random Forest; machne learnng algorthm; pruned; Unpruned; Abstract The research has been focused on retreval of relevant nformaton from the collecton of data s a challengng task n data mnng technques [4]. In ths research focuses on machne learnng algorthm that nvolves to fndng the pattern of health level among the collecton of kdney functon test to dagnoss and decson makng. Data mnng s a process or method that extracts nterestng knowledge from small or large amounts of dataset, here machne learnng algorthm mplemented for allow a computer to learn whch nvolves conscousness. An objectve of the research s to comparng decson tree algorthm of Random Forest, C4.5 wth Bootstrap Aggregaton algorthm for analysng the tme effcency and accuracy of the requred algorthm. An experment result s observed the performance of selectng attrbute by usng two algorthms and analyse the better algorthm n classfcaton. Copyrght 201x Internatonal Journals of Multdscplnary Research Academy. All rghts reserved. Author correspondence: Dr. M. Maylvaganan Assocate Professor, Department of Computer Scence, PSG College of Arts and Scence, Combatore. research.gate@gmal.com Introducton In ths research, to fnd the kdney functon test, the blood Test for estmate GFR of patents. Blood wll be tested for a waste product called creatnne. Creatnne comes from muscle tssue. When the kdneys are damaged, they have trouble removng creatnne from your blood. Testng for creatnne s only the frst step. Next, the creatnne result s used n a math formula wth your age, race, and gender to fnd out your glomerular fltraton rate (GFR). Kdney functon tests are conducted to know whether all parameters of kdney are functonng wthn the normal range or not. These tests tell us what s the level of blood urea, creatnne, urc acd and * Assocate Professor ** Assstant Professor Department of Computer Scence, PSG College of Arts and Scence, Combatore. 249

2 other mnerals n the body. The normal values of ths test ranges are represents n the gven below n the table 1. Ths test estmates how well the kdneys are flterng waste. Any result lower than 60 mlllters/mnute/1.73m 2 may be a warnng sgn of kdney dsease. GFR (Glomerular Fltraton Rate) s a measure of kdney functon and s performed through a blood test. Scope of Research Methodology To propose an effcent framework for analyzng the collecton of patents data of kdney functon test to determnes the rate by lookng at factors, such as specfcally creatnne levels, age, gender, race, heght, weght. The Serum test also nvolvng for the fndng the fluctuaton level of blood urea, creatnne, urc acd and other mnerals n the body under ndependent varable. Based on the glomerular fltraton rate (dependent varable) and based on gender wse, to classfy the pattern of kdney dsease test by comparng Random forest and C4.5 Bootstrap Aggregaton algorthm. Current comparatve studes asses the performance of the algorthms based on the results obtaned n tme effcency and accuracy. Data Collecton and Pre-Processng In Preprocessng, the raw data of numercal data s converted nto nomnal data for classfyng process. After the preprocess, apply three algorthm of machne learnng technque of Random forest, C4.5 algorthm and C4.5 Bootstrap aggregaton algorthm for analysng the accuracy of classfcaton process. Table 1. Collecton of Patents data for fndng Glomerular Fltraton Rate of Kdney Functon test Cases Age Heght (Cm) Weght (Kg) Race Gender Whte M black M whte F black F Whte F Whte F Whte F black F black M black M Whte M Whte M Whte M black M black M black F black F Whte F Whte M Whte M 250

3 Table 3: CKD EPI Equaton for Estmatng GFR Expressed for Specfed Race, Sex and Serum Creatnne n mg/dl Race Sex Serum Creatnne, S cr (mg/dl) Equaton (age n years for 18) Black Female 0.7 GFR = 166 (S cr /0.7) (0.993) Age Black Female > 0.7 GFR = 166 (S cr /0.7) (0.993) Age Black Male 0.9 GFR = 163 (S cr /0.9) (0.993) Age Black Male > 0.9 GFR = 163 (S cr /0.9) (0.993) Age Whte or other Female 0.7 GFR = 144 (S cr /0.7) (0.993) Age Whte or other Female > 0.7 GFR = 144 (S cr /0.7) (0.993) Age Whte or other Male 0.9 GFR = 141 (S cr /0.9) (0.993) Age Whte or other Male > 0.9 GFR = 141 (S cr /0.9) (0.993) Age Table 4: Data Collecton of serum test n kdney functon Cases C U TP A UA P CAL BIC PO Sod

4 Medcal Dagnoss of Kdney test Preprocessng Tranng data Machne Learnng: Random Forest and C4.5 wth Bootstrap Aggregaton Create Classfer based on tree Identfy an nformaton gan and gan rato Analyss Identfy the Classfcaton Accuracy of Proposed method Result and Dscusson Fg. 1 Frame work of Research Methodology Fg 1 represents the medcal dagnoss of kdney functon analyss wth respect to the collecton of Creatne, Urea ntrogen, total proten, Albumn, Urc acd n gender wse, Phosphate, Calcum, B- Carbonate, Potassum, Sodum, Serum n gender wse for analysng the fluctuaton of ranges as shown n table 2, when predctng the data based on the Glomerular Fltraton Rate t can dentfy the patent health condton early. After preprocessng, apply machne learnng technque of Random forest and C4.5 Bootstrap aggregaton algorthm for analysng the accuracy of classfcaton process. Table 2: Ranges of Serum test Kdney Serum Test for Kdney Functon Ranges Low Normal Hgh Unts Creatne < >1.4 mg/dl Urea ntrogen < >20 mg/dl Total proten < >6.0 g/dl Albumn < >5 g/dl Urc Acd Female < >7 mg/dl Male < >3.5 mg/dl Phosphate < >4.5 mg/dl Calcum < >10.5 mg/dl B-carbonate < >30 meq/l Potassum < >5.5 meq/l Sodum < >145 meq/l Serum Creatnne Female < >1.1 mg/dl Male < >1.3 mg/dl 252

5 Table 2 represents the unt of mg/dl represents mllgrams per Declter, unt of g/dl represents gram/declter and unt of meq/l represents mll-equvalents per ltre. The decson tree can be constructed from the gven set of attrbutes. Greedy strategy that grows a decson tree by makng a seres of locally optmum decson about whch attrbutes to use for parttonng the data. Typcal methods of machne learnng algorthm of Random Forest and C4.5 wth Bootstrap aggregaton can be mplemented to obtan hgh classfcaton accuracy and tme effcency of medcal dagnoss data. The performance can be dentfed and dagnoss for kdney functon based on Serum test. Pre-prunng nvolves decdng when to stop developng sub-trees durng the tree buldng process. The mnmum number of observatons n a leaf can determne the sze of the tree. After a tree s constructed, the C4.5 rule nducton program can be used to produce a set of equvalent rules. Prunng produces fewer, more easly nterpreted results. Proposed Methodology Random Forest Algorthm A random forest s a collecton of unpruned decson trees [4]. It combnes many tree predctors, where each tree depends on the values of a random vector sampled ndependently. In order to construct a tree, assume that N s the number of tranng observatons and S s the number of attrbutes n a tranng set. In order to determne the decson node at a tree, choose N<<S as the number of varables to be selected. Select a bootstrap sample from the N observatons n the tranng set and use the rest of the observatons to estmate the error of the tree n the testng phase. Randomly choose m varables as a decson at a certan node n the tree and calculate the best splt based on the m varables n the tranng set. Trees are always grown and never pruned compared to other tree algorthms. C4.5 Algorthm wth Bootstrap Aggregaton (Baggng) Algorthm C4.5 s a decson tree technque whch s enhanced by ID3 algorthm. It s one of the most popular algorthm for rule base classfcaton [4]. Here an attrbutes can be splt nto two partton based on the selected threshold value, all the value satsfed by the constrant t wll be assgned n one chld and remanng values can be store n another chld respectvely. It also handles mssng values. Here t can be gather of all nomnal tests through entropy gan and the values are sorted based on the values n contnuous attrbute values whch are calculated n one scan. Ths process s repeated for each contnuous attrbutes when the process s termnated. Steps of the System: 1. Selectng dataset as an nput to the algorthm for processng. 2. Selectng the classfers. 3. Calculate entropy, nformaton gan, gan rato of attrbutes. 4. Processng the gven nput dataset accordng to the defned algorthm of C4.5 data mnng. 5. Accordng to the defned algorthm of mproved C4.5 data mnng processng the gven nput dataset. 6. The data whch should be nputted to the tree generaton mechansm s gven by the C4.5 and mproved algorthm C4.5 processors. Tree generator generates the tree for C4.5 and mproved C4.5 decson tree The rule set s formed from the ntal state of decson tree. Each path from the ntal state, the 253

6 condton wll be evaluate and smplfed by the effect of rule and an outcomes wll put on the requred leaf, the step wll contnuous when t comes dscardng the condton. Let freq (C, S) stand for the number of samples n S that belong to class C (out of k possble classes), and S denotes the number of samples n the set S. Then the entropy of the set of equaton 1 such as 3, k Info(s)= ((freq(c,s) / s ).log 2 (freq(c,s)/ s )) =1 (1) After set T has been parttoned n accordance wth n outcomes of one attrbute test X by equaton 2 and n S j Info x ( S).Info Sj (2) 1 S Gan(x) = Info(S) - Info x (S) (3) The gan rato normalzes the nformaton gan as followng equaton 4, InformatonGan(a,S) GanRato(a,S) = Entropy(a,S) (4) Pre-prunng nvolves decdng when to stop developng sub-trees durng the tree buldng process. To ntegrate C4.5 algorthm combnes wth Baggng mproves generalzaton error by reducng the varance of the base classfers. The performance of baggng depends on the stablty of the base classfer. After tranng the x classfers, a test nstance s assgned to the class that receves the hghest number of votes. Input: D, Set of S tranng tuples; Classfcaton learnng scheme: C4.5 Algorthm Output: The ensemble- a composte model, Z*. Algorthm: Baggng Let u be the number of bootstrap samples for j=1 to u do // create w models: Create a bootstrap sample of sze M, C by samplng C wth replacement; Use C and learnng scheme to derve a Model, Z ; endfor To use the ensemble to classfy a tuple Y: Let each of the u models classfy Y and return majorty vote; It ncreases accuracy because the composte model reduces the varance of the ndvdual classfers. Result and Dscusson Table 2 : Accuracy Comparson of Dfferent Machne learnng algorthm Algorthm Selected Senstvty Varables (%) (%) Random Forest 61.6% 58% C4.5 (Pruned) 71% 70.2% C4.5 (Unpruned) 63% 61% Table 2 represents the senstvty s the probablty that a test wll ndcate the classable of kdney functon test condton, Random forest acheved 56% of selected varable and 58% of senstvty varable. C4.5 Pruned acheved a classfcaton accuracy of 71% of selected varables and 70.2% of senstvty varable 254

7 and fnally C4.5 unpruned acheved a classfcaton accuracy of 63% n selected varable and 61% n senstvty varable. Senstvty: True postve/(true postve + False Negatve) 100 Specfcty s the fracton of those wthout dsease who wll have a negatve test result: Specfcty: True Negatve/(True Negatve + False Postve) 100 Here Senstvty and specfcty are characterstcs of the test. The selected varables alone were used to fnd the senstvty and specfcty of the data mnng algorthms. In C4.5 wth Bootstrap Aggregaton gves hgh accurate classfcaton rate than C4.5 pruned, unpruned and random forest algorthm. When compared wth C4.5 pruned and C4.5 wth Bootstrap aggregaton, t can be slghtly vary for observng to determne the sze of the tree. Table 3 : Accuracy Comparson of Dfferent Ensemble methods Algorthm Classfed Instance Accuracy Random Forest 70% 30% In classfed Instance Accuracy C4.5 wth Baggng Aggregaton 74.2% 25.8% Fg. Tree vew of C4.5 wth Baggng Aggregaton Table 3 represents an accuracy of classfcaton of nstance wth dfferent ensembles methods, n ths mplementaton 68% of data can be classfed n random forest and C 4.5 wth Baggng Aggregaton classfed 74.2 % of nstance. The classfcaton of Tree Vew based on C4.5 wth Baggng Aggregaton algorthm. Concluson From ths research, t can be concluded that to evaluate an accurate method of machne learnng technque by applyng medcal dagnoss data wth two contrbutons. From the research, C4.5 wth Bootstrap Aggregaton gves hgh accurate classfcaton rate rather than C4.5 pruned, C4.5 unpruned and random forest algorthm. References [1] Hall, Mark, et al. "The WEKA data mnng software: an update." ACM SIGKDD Exploratons Newsletter 11.1 (2009):

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