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1 econstor Make Your Publcatons Vsble. A Servce of Wrtschaft Centre zbwlebnz-informatonszentrum Economcs Chang, Huan-Cheng; Chang, Pn-Hsang; Tseng, Sung-Chn; Chang, Ch- Chang; Lu, Yen-Chao Artcle A comparatve analyss of data mnng technques for predcton of postprandal blood glucose: A cohort study Internatonal Journal of Management, Economcs and Socal Scences (IJMESS) Provded n Cooperaton wth: Internatonal Journal of Management, Economcs and Socal Scences (IJMESS) Suggested Ctaton: Chang, Huan-Cheng; Chang, Pn-Hsang; Tseng, Sung-Chn; Chang, Ch- Chang; Lu, Yen-Chao (2018) : A comparatve analyss of data mnng technques for predcton of postprandal blood glucose: A cohort study, Internatonal Journal of Management, Economcs and Socal Scences (IJMESS), ISSN , IJMESS Internatonal Publshers, Jersey Cty, NJ, Vol. 7, Iss. Specal Issue, pp Ths Verson s avalable at: Standard-Nutzungsbedngungen: De Dokumente auf EconStor dürfen zu egenen wssenschaftlchen Zwecken und zum Prvatgebrauch gespechert und kopert werden. Se dürfen de Dokumente ncht für öffentlche oder kommerzelle Zwecke vervelfältgen, öffentlch ausstellen, öffentlch zugänglch machen, vertreben oder anderwetg nutzen. Sofern de Verfasser de Dokumente unter Open-Content-Lzenzen (nsbesondere CC-Lzenzen) zur Verfügung gestellt haben sollten, gelten abwechend von desen Nutzungsbedngungen de n der dort genannten Lzenz gewährten Nutzungsrechte. Terms of use: Documents n EconStor may be saved and coped for your personal and scholarly purposes. You are not to copy documents for publc or commercal purposes, to exhbt the documents publcly, to make them publcly avalable on the nternet, or to dstrbute or otherwse use the documents n publc. If the documents have been made avalable under an Open Content Lcence (especally Creatve Commons Lcences), you may exercse further usage rghts as specfed n the ndcated lcence.

2 Internatonal Journal of Management, Economcs and Socal Scences Specal Issue-Internatonal Conference on Medcal and Health Informatcs (ICMHI 2017) 2018, Vol. 7(S1), pp ISSN A Comparatve Analyss of Data Mnng Technques for Predcton of Postprandal Blood Glucose: A Cohort Study Huan-Cheng Chang 1 Pn-Hsang Chang 2 Sung-Chn Tseng 3 * Ch-Chang Chang 4 Yen-Chao Lu 5 1 Dept. of Communty Medcne, Landseed Hosptal, Taoyuan, Tawan 2 Dept. of Healthcare Management, Yuanpe Unversty of Medcal Technology, Hsnchu, Tawan 3 Dv. of Famly Medcne, Chay Chang Gung Memoral Hosptal, Chay, Tawan 4 School of Medcal Informatcs, Chung-Shan Medcal Unversty/Hosptal, Tachung, Tawan 5 School of Nursng, Chung-Shan Medcal Unversty, Tachung, Tawan The use of advanced predctve technques and reasonng models has greatly asssted clncans n mprovng the dagnoss, prognoss, and treatment of dabetes. Although numerous studes have focused on the relatonshp between abnormal blood glucose levels and dabetes, few have focused on the rsk forecastng of postprandal blood glucose levels n patents wth dabetes. Ths work amed to develop a model for the predcton of postprandal blood glucose levels to screen for undagnosed dabetes cases n a cohort study. The performance of the proposed model was then compared wth those of fve other datamnng technques: random forest (RF), support vector machne (SVM), C5.0, multlayer perceptron (MLP), and logstc regresson (LR). The data of 1,438 patents who were admtted to Landseed Hosptal, Northern Tawan, over the perod of 2006 and 2013 were collected and used to evaluate the performances of the data-mnng technques. Compared wth the 4.5, SVM, MLP, and LR models, the RF model had the best predcton capablty for postprandal blood glucose levels n terms of the overall correct classfcaton rate. The results of ths study underscore the mportance of dentfyng the preclncal symptoms of abnormal blood glucose levels. The proposed model provdes precse reasonng and predcton and can be used to help physcans mprove the dagnoss, prognoss, and treatment of patents wth dabetes. Keywords: Data mnng technques, random forest, support vector machne, multlayer perceptron, logstc regresson The global prevalence of dabetes melltus, commonly referred to as dabetes, has drastcally ncreased (Lu et al., 2017). Consequently, dalyss treatment for dabetc nephropathy has become a large burden on the natonal health nsurance of Tawan. The early dagnoss of the rsk factors related to changes n postprandal blood glucose levels could help prevent or delay dabetc nephropathy. Moreover, early dagnoss may Manuscrpt receved May 24, 2017; revsed June 20, 2017; accepted July 20, The Author(s); CC-BY-NC; Lcensee IJMESS *Correspondng author: changntw@gmal.com 132

3 Internatonal Journal of Management, Economcs and Socal Scences mprove the outcomes of patents wth dabetes, and the regular screenng of blood glucose levels and blood pressure can decrease the ncdence of dabetes. Screenng for undagnosed dabetes through blood samplng, however, s prohbtve because of the hgh costs and nvasveness of the technque. Accurate and precse reasonng and predcton models may greatly help physcans mprove the dagnoss, prognoss, and treatment of dabetes. Several studes have been conducted to clarfy the response of glucose levels n dabetc patents to varous stmul. Several factors affect the postprandal levels of blood glucose. These factors nclude age, weght, wast grth, whte blood red blood cell counts, and globuln, hgh-densty lpoproten, and urne red blood cell concentratons. Data-mnng technques have been wdely used to predct blood glucose levels. The use of data-mnng technques to construct predcton models for blood glucose levels does not requre strong model assumptons and can capture delcate underlyng patterns and relatonshps n emprcal data, hence provdng promsng results for the predcton of blood glucose levels. Although data-mnng technques have been utlzed n numerous studes to predct fastng blood glucose and/or postprandal blood glucose levels, few studes have attempted to utlze data-mnng technques to predct or classfy postprandal blood glucose as normal or abnormal. Moreover, most exstng studes on blood glucose levels n dabetc patents are based on a contnuous glucose montorng system, a devce that s nstalled on the patent for measurng the patent s blood glucose over specfc ntervals. To the best of our knowledge, no study has utlzed datamnng for the predcton of postprandal blood glucose levels n a cohort study. Therefore, a model for the predcton of postprandal blood glucose levels was proposed and desgned n ths study. The predctve performance of the proposed model was compared wth those of fve data-mnng technques. The fve data-mnng methods used n ths study are random forest (RF), support vector machne (SVM), C5.0, multlayer perceptron (MLP), and logstc regresson (LR). RF s an ensemble learnng method that grows multple random tree classfcatons to generate an overall classfcaton. SVM s based on statstcal learnng theory and s derved from the structural rsk mnmzaton prncple for estmatng a hyperplane for classfcaton. C4.5 s a non-parametrc and fast classfcaton technque that adopts a greedy approach and uses a top-down recursve dvde-and-conquer strategy to construct a decson tree. MLP s a neural network 133

4 Chang et al. commonly used to solve classfcaton problems and s traned wth a backpropagaton algorthm. MLP also utlzes a supervsed learnng technque to transform sets of nput data nto a desred output. LR s a wdely used statstcal modelng technque that s a specal case of the lnear regresson model. The major advantage of ths approach s that t can produce a smple probablstc formula of classfcaton. These fve data-mnng technques have been used to predct blood glucose levels. However, to the best of our knowledge, these fve models have not been used to predct postprandal blood glucose levels n a cohort study. Tresp et al. (1999) utlzed recurrent neural networks and tme-seres convoluton neural networks to predct the blood glucose levels of patents wth dabetes. The recurrent neural network combned wth the lnear error model exhbted excellent performance and outperformed the compartment and tme-seres convoluton neural-network models. Wang et al. (2016) used an mproved grey (1, 1) model to predct the postprandal blood glucose levels of patents wth type 2 dabetes usng lmted data. The mproved grey model outperformed the autoregressve (AR) model n the predcton of blood glucose levels. Wang and An (2014) appled a least-squares-based AR model to predct blood glucose levels. The model accurately llustrated the changes n blood glucose levels to provde an early warnng for the occurrence of low blood glucose. García-Jaramllo et al. (2013) adopted and compared the performance of three nterval models n predctng the postprandal blood glucose levels of patents wth type 1 dabetes under the condtons of uncertanty and ntra-patent varablty. The rest of ths paper s organzed as follows. A bref revew of related works s presented n Secton 2. RF, SVM, C4.5, MLP, and LR are ntroduced n Secton 3. The expermental results are provded n Secton 4, and the concluson s provded n Secton 5. METHODOLOGY -Random Forest Random forest (RF) s a supervsed machne learnng algorthm whch combnes classfcaton method based on the un-weghted majorty of class votes (Breman, 2001). In a RF, frst, multple random samples of varables are selected as the tranng dataset usng the baggng procedure. The baggng procedure means 134

5 Internatonal Journal of Management, Economcs and Socal Scences random samplng wth replacement whch s a meta-algorthm can be used to reduce varance and ads to elude over-fttng synchronously. Then, the tree-type classfers correspondng to selected samples are constructed n the data tranng process. A large number tree makes RF from the selected samples. Fnally, all classfcaton trees are combned and fnal classfcaton results are obtaned by votng on each class and then choosng the wnner class n terms of the number of votes to t. The RF performance measure by a metrc called out of bag error calculated as the average of the rate of error n each weak learner. In RF, each ndvdual tree s explored n a partcular way. Frst, gven a set of tranng data N, n random samples wth repetton (Bootstrap) are taken as tranng set by usng baggng procedure. Then, for each node of the tree, M nput varables are determned, and m varables (<<M) are selected for each node. The most mportant varable randomly chosen s used as a node. The value of m remans constant. Fnally, each tree s developed to ts maxmum expanson. Please refer to Breman (2001) for more detal nformaton of RF. -Support Vector Machne The basc dea of SVM ntally, lnearly or non-lnearly, map the nput vectors nto a hgher dmensonal feature space. Then, SVM seeks an optmzed hyperplane to separate two classes n the feature space. A x descrpton of SVM algorthm s follows. Let, y N 1, x R d y 1,1, s the tranng set wth nput vectors and labels. Where, N s the number of sample observatons and d s the dmenson of each observaton, y s known target. SVM s to seek the hyperplane w x b 0, where w s the vector of hyperplane and b s a bas term, to separate the data from two classes wth maxmal margn wdth 2 2/ w, and the all ponts under the boundary s named support vector. For optmal the hyperplane, SVM s to solve the followng optmzaton problem (Vapnk 2000). Mn 1 ( x) w 2 2 (1) S.t. T y ( w x b) 1, 1, 2,..., N As t s hard to solve eq.(1), t s transformed to be dual problem by usng Lagrange method. The value of n the Lagrange method must be non-negatve real coeffcents. The eq. (1) s transformed nto the followng constraned form, 135

6 Chang et al. Max N N 1 T ( w, b,,, ) y y x x 1 2 1, j 1 j j j (2) S.t. N j 1 j y j 0, 0 C, 1, 2,..., N In eq. (2), C s the penalty factor and vewed as a tunng parameter whch can be used to control the tradeoff between maxmzng the margn and the classfcaton error. In general, t could not fnd the lnear separate hyperplane n all applcaton data. In the non-lnear data, t must transform the orgnal data to hgher dmenson of lnear separate s the best soluton. The hgher dmenson s called feature space, t mprove the data separated by classfcaton. The common used kernel functon s radal bass functon (RBF). It s appled n ths study. For more detals about SVM, please refer to Vapnk (2000). -C 4.5 C4.5 classfer s a process for the classfcaton and retreves useful nformaton n the form of a decson tree. The algorthm adopts a greedy approach n whch the decson trees are constructed n a top-down recursve dvde and conquer manner on the bass of a tranng set (Qunlan 1993). C4.5 bulds decson trees from a set of tranng data based on the concept of nformaton entropy. The tranng data s a set of already classfed samples. Each sample s a vector ncludng attrbutes or features. The tranng data s augmented wth a vector representng the class that each sample belongs to. Each attrbute of the data can be used to make a decson. C4.5 examnes the normalzed nformaton gan that results from choosng an attrbute for splttng the data. The attrbute wth the hghest normalzed nformaton gan s the one used to make the decson. The algorthm then recurs on the smaller sub-lsts. For more detals about C4.5, please refer to (Larose 2005). -Multlayer Perceptron Multlayer Perceptron (MLP) s ganed ther popularty due to t s a smple archtecture but a powerful problem-solvng ablty. Back propagaton s a general supervsed method for teratvely calculatng the weghts and bases of the MLP. Ths type of model s termed BPN. BPN uses a steepest descent technque wth learnng and momentum terms. A BPN topology conssts of a number of nodes (neurons) connected by lnks and conssts of three layers: nput layer, hdden layer(s) and output layer. The nodes n the nput layer 136

7 Internatonal Journal of Management, Economcs and Socal Scences receve nput sgnals from an external source and the nodes n the output layer provde the target output sgnals. Any layers between nput and output layers are called hdden layers. Snce one hdden layer network s suffcent to model any complex system wth desred accuracy the desgned BPN model n ths study wll have only one hdden layer. A three-layer BPN s used n ths study. In a BPN topology, each layer comprses several neurons that are nterconnected by sets of weghts. The neurons obtan nputs from ntal nputs or nterconnectons and generated outputs usng a nonlnear transfer functon. BPN uses gradent steepest descent tranng algorthm to mnmze error and adjusts nterconnecton weghts. For the gradent descent algorthm, the step sze, called the learnng rate, must be specfed frst. The learnng rate s crucal for BPN snce smaller learnng rates tend to slow down the learnng process before convergence whle larger ones may cause network oscllaton and unable to converge. Please refer Haykn (1999) for more detals about MLP. -Logstc Regresson LR s smlar to a lnear regresson model but s suted to models where the dependent varable s dchotomous. A logstc regresson model specfes that an approprate functon of the ftted probablty of the event s a lnear functon of the observed values of the avalable explanatory varables. In producng the LR equaton, the maxmum-lkelhood rato was used to determne the statstcal sgnfcance of the varables. LR s useful for stuatons n whch can be able to predct the presence or absence of a characterstc or outcome based on values of set of predctor varables. LR model for p ndependent varables can be wrtten as where s probablty of presence. And are regresson coeffcents. There s a lnear (3) model hdden wthn the logstc regresson model. The natural logarthm of the rato of to gves a lnear model n : (4) The, has many of the desrable propertes of a lnear regresson model. The ndependent varables can be a combnaton of contnuous and categorcal varables.for more detals about logstc regresson, please refer to Hosmer et al. (2013). 137

8 Chang et al. Data Collecton We collected data from LandSeed Hosptal for After excludng some follow-up records (e.g., records for patents age<18), we obtaned records for patents who vsted the hosptal on three separate occasons over the perod of 2006 and 2013 for two consecutve years. The patents had normal postprandal blood glucose levels at the frst vst and may have abnormal postprandal blood glucose levels at the thrd vst. The data ncluded 1438 clncal follow-up records. Of these records, 438 patents reported abnormal postprandal blood glucose at the thrd vst. Prevous studes have studed the ncdence and rsk factors assocated wth dabetes. In ths study, each subject n the dataset contaned 29 predctor varables, as shown n Table 1, and the response varable s whether the postprandal level of blood glucose s normal or not. The performances of the fve data-mnng methods were evaluated usng the 10-fold cross-valdaton method. The data-mnng software WEKA, whch was developed by Frank et al. (2016), was utlzed to develop the RF, C4.5, SVM, MLP, and LR models wth default settngs for each algorthm. RESULTS Senstvty and specfcty are the two mportant measures n medcal/healthcare classfcaton. Senstvty (also called the true-postve rate) s a measure of the proporton of postves that are correctly dentfed, and specfcty (also called the true-negatve rate) s a measure of the proporton of negatves that are correctly dentfed. The correct classfcaton rate (CCR), senstvty, and senstvty were used as the three ndexes for judgng the performance of the fve classfcaton methods. The classfcaton results for postprandal blood glucose levels (the confuson matrx) predcted by the RF model are summarzed n Table 1. From the results presented n Table 1, we can observe that the overall CCR s 82.68%. That s, {1-1} s 923 (CCR of 92.30%) and {2-2} s 266 (CCR of 60.73%). {1-1} represents senstvty and ndcates that a class 1 subject, whch s a subject wth normal postprandal blood glucose levels, s correctly classfed nto class 1. {2-2} represents specfcty and ndcates that a class 2 subject, whch s a subject wth abnormal postprandal blood glucose levels, s correctly classfed nto class 2. Tables 2 5 show the classfcaton results of C4.5, SVM, MLP, and LR, respectvely. Table 2 shows that 138

9 Internatonal Journal of Management, Economcs and Socal Scences the overall CCR of the C4.5 method s 76.56% wth a senstvty of 85.90% and specfcty of 55.25%. Table 3 depcts that the CCR of the SVM method s 69.61% wth a senstvty of 99.20% and specfcty of 2.05%. The CCR, senstvty, and specfcty of MLP model are 75.73%, 83.20%, and 56.68%, respectvely, as shown n Table 4. As shown n Table 5, the CCR of the LR method s 74.48% wth a senstvty of 84.00% and specfcty of 52.74%. Classfed Class Actual Class 1 (normal) 2 (abnormal) 1 (normal) 923 (92.30%) 77 (7.70%) 2 (abnormal) 172 (39.27%) 266 (60.73%) Overall CCR : 82.68% Table 1. Classfcaton Results Usng RF Model Classfed Class Actual Class 1 (normal) 2 (abnormal) 1 (normal) 529 (85.90%) 141 (14.10%) 2 (abnormal) 196 (44.75%) 242(55.25%) Overall CCR : 76.56% Table 2. Classfcaton Results Usng C4.5 Model Classfed Class Actual Class 1 (normal) 2 (abnormal) 1 (normal) 992(99.20%) 8 (0.80%) 2 (abnormal) 429 (97.95%) 9(2.05%) Overall CCR : 69.61% Table 3. Classfcaton Results Usng SVM Model Classfed Class Actual Class 1 (normal) 2 (abnormal) 1 (normal) 832(83.20%) 168 (16.80%) 2 (abnormal) 181 (41.32%) 257(56.68%) Overall CCR : 75.73% Table 4. Classfcaton Results Usng MLP Model The summarzed results of the fve constructed models are shown n Table 6 and were used to evaluate ther capablty to predct postprandal blood glucose levels. From the data shown n the Table, we can conclude that the RF model has the best capablty to predct postprandal blood glucose levels n terms of 139

10 Chang et al. the overall CCR. The SVM model has the hghest senstvty of 99.20% but has the lowest specfcty of 2.05%. The RF model generated the hghest specfcty of 60.73% and the second-hghest senstvty of Classfed Class Actual Class 1 (normal) 2 (abnormal) 1 (normal) 840(84.00%) 160 (16.00%) 2 (abnormal) 207 (47.26%) 231(52.74%) Overall CCR: 74.48% Table 5. Classfcaton results usng LR model %. The RF model outperformed the fve models n specfc and general stuatons, ndcatng that t has better classfcaton accuracy than the other fve approaches. Therefore, the RF model s an effectve alternatve model for the predcton of postprandal blood glucose levels. Algorthms Overall CCR Senstvty {1-1} Specfcty {2-2} RF 82.68% 92.30% 60.73% C % 85.90% 55.25% MLP 75.73% 83.20% 56.68% LR 74.48% 84.00% 52.74% SVM 69.61% 99.20% 2.05% Table 6. Classfcaton Results of the Fve Data Mnng Models CONCLUSION Accurate and precse reasonng and predcton models greatly help physcans mprove the dagnoss, prognoss, and treatment of dabetes. We used fve data-mnng technques and desgned a model for the predcton of postprandal blood glucose levels on the bass of the known rsk factors of dabetes. The results showed that the RF approach exhbted the hghest classfcaton accuracy out of the fve models. Its specfcty and overall CCR were hgher those of the C4.5, SVM, MLP, and LR models. Therefore, the RF model provdes better classfcaton accuracy than the other competng approaches and s an effectve datamnng method for the predcton of postprandal blood glucose levels n a cohort study. REFERENCES 140

11 Internatonal Journal of Management, Economcs and Socal Scences Wang, Y., We, F., Sun, C. & L, Q. (2016). The Research of Improved Grey GM (1, 1) Model to Predct the Postprandal Glucose n Type 2 Dabetes. BoMed Research Internatonal, Retreved Nov. 1, 2017, from Wang, Y. & An, B. (2014). The research least squares based on AR model of glucose predcton. Advanced Materals Research, 10( ): Tresp, V., Bregel, T. & Moody, J. (1999). Neural-network models for the blood glucose metabolsm of a dabetc. IEEE Transactons on Neural Networks, 10(5): García-Jaramllo, M., Calm, R., Bonda, J., & Vehí, J. (2012). Predcton of postprandal blood glucose under uncertanty and ntrapatent varablty n type 1 dabetes: a comparatve study of three nterval models. Computer methods and programs n bomedcne, 108(1): Yamaguch, M., Kaseda, C., Yamazak, K., & Kobayash, M. (2006). Predcton of blood glucose level of type 1 dabetcs usng response surface methodology and data mnng. Medcal and Bologcal Engneerng and Computng, 44(6): Georga, E., Protopappas, V., Gullen, A., Fco, G., Ardgo, D., Arredondo, M. T. & Fotads, D. I. (2009). Data mnng for blood glucose predcton and knowledge dscovery n dabetc patents: The METABO dabetes modelng and management system. Conference proceedngs: Annual Internatonal Conference of the IEEE Engneerng n Medcne and Bology Socety. IEEE Engneerng n Medcne and Bology Socety, 2009, Ovedo, S., Vehí, J., Calm, R., & Armengol, J. (2016). A revew of personalzed blood glucose predcton strateges for T1DM patents. Internatonal Journal for Numercal Methods n Bomedcal Engneerng, 33(6): e2833. Zarkogann, K., Mtss, K., Ltsa, E., Arredondo, M. T., Fcο, G., Foravant, A., & Nkta, K. S. (2015). Comparatve assessment of glucose predcton models for patents wth type 1 dabetes melltus applyng sensors for glucose and physcal actvty montorng. Medcal & bologcal engneerng & computng, 53(12): Fernando, W. C. T. (2016). Blood glucose predcton models for personalzed dabetes management. Unpublshed doctoral dssertaton, North Dakota State Unversty, Unted States. Vapnk, V. N. (2000) The Nature of Statstcal Learnng Theory. Berln: Sprnger. Breman, L. (2001). Random forests. Machne learnng, 45(1): Larose, D. T. (2005). Dscoverng Knowledge n Data: An Introducton to Data Mnng. New Jersey: John Wley & Sons. Haykn, S. (1999). Neural network: A comprehensve foundaton. Englewood Clffs. NJ: Prentce Hall. Hosmer, D. W., Lemeshow, S., & Sturdvant, R. X. (2013). Appled logstc regresson. NJ: John Wley & Sons. Qunlan, J. R. (1993) C4.5: programs for machne learnng. San Francsco: Morgan Kaufnann. Frank, E., Hall, M. A. & Wtten, I. H. (2016). The WEKA Workbench. In Wtten, I. H., Frank, E., Hall, M. A. & Pal, C. J. (2016), Data Mnng: Practcal Machne Learnng Tools and Technques (4th. Ed.), San Francsco: Morgan Kaufmann. Lu, Q., L, W., Xue, M., Chen, Y., Du, X., Wang, C., Han, L., Tang, Y., Feng, Y., Tao, C. & He, J-Q. (2017). Dabetes melltus and the rsk of multdrug resstant tuberculoss: A Meta-analyss. Scentfc Reports, 7(1), Retreved Nov 1, 2017, from ACKNOWLEDGMENT Ths work s supported by the Chung Shan Medcal Unversty Hosptal and LandSeed Hosptal: CSMU-LSH