Clustering and Classifying Diabetic Data Sets Using K-Means Algorithm

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1 Article ca be accessed olie at Clusterig ad Classifyig Diabetic Data Sets Usig K-Meas Algorithm M. Kothaiayaki*, P. Thagaraj** Abstract The k-meas algorithm is well kow for its efficiecy i clusterig large data sets. However, workig oly o umeric values prohibits it from beig used to cluster real world data cotaiig categorical values. I this paper we preset the Classificatio of diabetic s data set ad the k-meas algorithm to categorical domais. Before classify the data set preprocessig of data set is doe to remove the oise i the data set. We use the missig value algorithm to replace the ull values i the data set. This algorithm is also used to improve the classificatio rate ad cluster the data set usig two attributes amely plasma ad pregacy attribute. Keywords: Classificatio, Cluster Aalysis, Clusterig Algorithms, Categorical Data, Pre-processig. Itroductio Classificatio is a mechaism to classify the data set ad ame the classes. After classificatio calculate the classificatio rate usig the formula. Usig this algorithm the data set is classified ito two class label amely tested_ positive ad tested_egative. The data set is cotaiig ie attributes amely preg, plas, mass, age, isu, ski, pedi, pres ad class. Partitioig a set of objects i databases ito homogeeous groups or clusters is a fudametal operatio i data miig. Clusterig is a popular approach to implemetig the partitioig operatio. Clusterig methods partitio a set of objects ito clusters such that objects i the same cluster are more similar to each other tha objects i differet clusters accordig to some defied criteria. The data sets to be mied ofte cotai millios of objects described by tes, hudreds or eve thousads of various types of attributes or variables (iterval, ratio, biary, ordial, omial, etc.). This requires the data miig operatios ad algorithms to be scalable ad capable of dealig with differet types of attributes. I this paper we preset algorithms that use to classify the data set ito two classes ad compare with stadard. The k-meas to cluster data havig categorical values. 2. Literature Review A lot of research work has bee doe o various medical data sets icludig Pima Idia diabetes dataset. The authors [6] has implemeted their algorithm ad achieved the accuracy i classifyig ad clusterig the diabetics datasets. I their experimet, they elimiated Icorrect labeled istace by usig K-meas clusterig followed by feature extractio usig GA_CFS. The resultat dataset is divided ito traiig data ad test data usig ratio. Experimets were carried out for differet values of k ragig k from to 5. The accuracy Diabetic data set usig proposed method without feature selectio is 95.56% with k = 5. D. Vijayalakshmi & K. Thilagavathi has aalysis that the clusterig algorithm based o a graph b-colorig techique was used to cluster Pima Idia diabetic dataset. They implemeted, performed experimets, ad compared with KNN Classificatio ad K-meas clusterig. The results show that the clusterig based o * Assistat Professor, Departmet of Computer Applicatios, Baari Amma Istitute of Techology, Sathyamagalam, Tamil Nadu, Idia. kothaimk@gmail.com ** Professor & Head, Departmet of Computer Sciece ad Egieerig, Baari Amma Istitute of Techology, Sathyamagalam, Tamil Nadu, Idia.

2 24 Joural of Applied Iformatio Sciece Volume Issue Jue 203 graph colorig out performace tha the other clusterig approach i terms of accuracy ad purity. The mai purpose of the Diabetic Patiets Databases system [3] is to guide diabetic patiets durig the disease. Diabetic patiets could beefit from the diabetes expert system by eterig their daily glucoses rate ad isuli dosages; producig a graph from isuli history; cosultig their isuli dosage for ext day. The diabetes expert system is ot oly for diabetic patiet, but also for the people who suspect if they are diabetic. It s also tried to determie a estimatio method to predict glucose rate i blood which idicates diabetes risk. 3. Notatio We assume that i a database objects from the same domai are represeted by the same set of attributes, A, A2,. Am. Each attribute Ai describes a domai of values, deoted by DOM(Ai), associated with a defied sematic ad data type. Differet defiitios of data types are used i data represetatio i databases ad i data aalysis. A umeric domai is represeted by cotiuous values. A domai DOM(Aj) is defied as categorical if it is fiite ad uordered. A special value, deoted by, is defied o all categorical domais ad used to represet missig values. It meas the two objects have equal values for the attributes A,A2,,Am. For example, two patiets i a data set may have equal values for the attributes Age, Sex, Disease ad Treatmet. 4. The Classificatio Algorithm The classificatio algorithm is used to classify the data set ad amed the class label. Before classificatio, the data are preprocessed to remove the ull values. We used the missig values algorithm to remove the ull values. Istace of ull values, replace ito the mea value of each attribute. The algorithm is checked the plasma level ad segregate the class with sequece of coditio like age is less tha 27 ad mass is less tha 37 etc. the attributes are declared ad retrieved from the database. Usig this algorithm, all the istaces are classified ito two class label amely tested_positive ad tested_ egative. I this algorithm tested usig the 20 sample data ad classificatio is achieved for that sample data. 5. The k-meas Algorithm The k-meas algorithm is the mostly used clusterig algorithms, is classified as a partitioal or ohierarchical clusterig method. Give a set of umeric objects X ad a iteger umber k ( ),the k-meas algorithm searches for a partitio of X ito k clusters that miimizes the withi groups sum of squared errors (WGSS). Miimize PW (, Q) = wil, d( Xi, Ql) k  l= i= k Subject to  wi, l i l= w i,l {0,}, i, l k 6. Experimetal Results 6.. Classificatio Performace The dataset are stored i the database with 0 fields ad data relevat to that field. The age is very importat to idetify the diabetics for the perso. The data set is cotaiig te attributes amely ame, preg, plas, mass, age, isu, ski, pedi, pres ad classlab. The data set is classified usig the algorithm ad attai the result may tested_positive or tested_egative. This result is compare with the origial classlab of that specific data set, if both are matches we classify the exactly, the its cout as True Positive. Likewise cout the values for True Negative (TN), False Positive (FP) ad False Negative (FN).The calculate the classificatio rate usig this formula: Precisio = TP / (TP + FP) Recall = TP / (TP + FN) Measure = 2*TP/ (2*TP+FP + FN) RECALL is the ratio of the umber of relevat records retrieved to the total umber of relevat records i the database. It is usually expressed as a percetage. PRECISION is the ratio of the umber of relevat records retrieved to the total umber of irrelevat ad relevat records retrieved. It is usually expressed as a percetage. True Positive meas that the data is exactly classified. False Positive meas that a uexpected result is achieved after classificatio doe. False Negative meas that the missig value of the classificatio. It meas some of the

3 Clusterig ad Classifyig Diabetic Data Sets Usig K-Meas Algorithm 25 values caot be classified. True Negative meas that the correct classificatio of the absece of result. I took 20 samples to test this algorithm, it exactly classify the all the samples. This algorithm eed to classify the data set has 768 istaces, each beig described by 0 attributes. The istaces were classified ito two classes, approved labeled as tested_egative ad tested_positive Clusterig Performace The primary use of clusterig algorithms is to discover the groupig structures iheret i data. The advatage of this approach is the structures of costructed data sets ca be cotrolled. Cluster Cluster 2 Tested_egative Tested_positive 8 50 This table is obtaied usig WEKA tool. It also clusters the data set accordig to this result. The 768 sample data set ad its clustered ito 3 cluster usig the distace measure. Before clusterig the pre processig is doe usig ormalizatio method. I this algorithm usig distace measure, the dataset are clusterig ito three groups. Iitialize the cluster values at radomly ad cluster the remaiig values usig distace formula. I WEKA 7.6 tool, classified this data set at 70 % of classificatio rate. So ow it s classified usig some criteria which is used to icrease the classificatio rate. Â i= d( XiQ, ) = d ( xi, j, qj) 6.3. Output m ÂÂ i= j= This shows the output for sample 30 diabetic s dataset. The data are saved i the Ms-Access. The database is coected through JDBC ad retrieved. The data are processed ad calculate the classificatio rate. D:\JAVA\JDK.3\bi>javac classificatiorate.java D:\JAVA\JDK.3\bi>java classificatiorate Attemptig to load JDBC Driver... JDBC Driver loaded... Coectig to database... Database coectio established i=30 Coectio to DB closed. Data Retrieved Successfully! ***Classificatio Result*********** tested_egative ai tested_egative devi tested_egative kavi tested_positive pavi tested_egative mai tested_egative vimal tested_egative ravi tested_egative kumar tested_positive jasi tested_egative jaaki tested_positive sathiya tested_positive sudar tested_egative guka tested_egative kaika tested_egative dhivya tested_egative murali tested_egative sakar tested_egative yuvi The classificatio rate is: % ****Clusterig Result************** Data is classified ito 3 clusters as follows. Cluster

4 26 Joural of Applied Iformatio Sciece Volume Issue Jue Cluster Cluster Coclusios Clusterig result for 30 samples The most attractive property of the k-meas algorithm i data miig is its efficiecy i clusterig large data sets. Classificatio is a data miig techique used to predict group membership for data istaces. The classificatio is doe usig this algorithm ad successfully classified the data set ito two class labels amely tested_positive ad tested_egative. The clusterig performace of the two algorithms has bee evaluated usig two real world data sets. The satisfactory results have demostrated the effectiveess of the algorithms i discoverig structures i data This paper has focused o the techical issues of extedig the k-meas algorithm to cluster the diabetic s data set ad classify the dataset. After that, usig this algorithm calculate the classificatio rate. For the 20 sample data set it gives 00% classificatio rate. For the whole data set it must be improved ad reached that level. The proposed algorithm, is used to improved the classificatio rate ad achieve the 00% result. Also efficietly cluster the dataset usig k-modes algorithm ad combied k-meas ad k-modes algorithm. It maily help to improve the efficiecy of the clusterig the dataset. Refereces. Huag, Z. (998). Extesios to the k-meas Algorithm for Clusterig Large Data Sets with Categorical Values, Data Miig ad Kowledge Discovery, 2, Mitchell, T. (997). Decisio Tree Learig (52-78). McGraw-Hill Compaies, Ic. 3. Yasodha, P. & Kaa, M. (20). Aalysis of a populatio of diabetic patiets databases i Weka tool. Proceedigs of the Iteratioal Joural of Scietific & Egieerig Research, 2(5). 4. Editorial, Diagosis ad Classificatio of Diabetes Mellitus, America Diabetes Associatio, Diabetes Care. (2004). 27(). 5. Karegowda, A. G., Puya, V., Majuath, A. S. & Jayaram, M. A. (202). Rule based classificatio for diabetic patiets usig cascaded K-meas ad decisio tree C4.5. Iteratioal Joural of Computer Applicatios, 45(2), ( ). 6. Karegowda, A. G., Jayaram, M. A. & Majuath, A. S. (202). Cascadig K-meas clusterig ad K-earest eighbor classifier for categorizatio of di-

5 Clusterig ad Classifyig Diabetic Data Sets Usig K-Meas Algorithm 27 abetic patiets. Iteratioal Joural of Egieerig ad Advaced Techology, (3). 7. Wu, C., Steibauer, J. R. & Kuo, G. M. (2005). EM Clusterig Aalysis of Diabetes Patiets Basic Diagosis Idex. Articles from AMIA Aual Symposium Proceedigs are provided here courtesy of America Medical Iformatics Associatio. 8. Maseri, W., Mohd, W., Herawa, T. & Ahmad, N. (203). Applyig Variable Precisio Rough Set for Clusterig Diabetics Dataset. I: AST203 ad Soft-tech 203 Iteratioal Coferece. 9. Vijayalakshmi, D. & Thilagavathi, K. (202). A Approach for Predictio of Diabetic Disease by Usig b-colourig Techique i Clusterig Aalysis. Proceedigs of Iteratioal Joural of Applied Mathematical Research, (4),