PDSS: The decision support system of diabetic patient for Public Health

Transcription

1 Proceedigs of the 3rd Iteratioal Coferece o Idustrial Applicatio Egieerig 5 PDSS: The decisio support system of diabetic patiet for Public Health Bejapuk Jogmuewai, Kailas Bumrugchat, Papo kaewhi Iformatics Program, Rajabath Nakhoratchasima Uiversity 34 Suraarai road., Muag District, Nakhoratchasima, 3, Thailad bejapuk.j@rru.ac.th, kailas.ifor@gmail.com, ampozoe_7@hotmail.com Abstract The decisio support system for data aalysis of diabetic patiet to risk complicatios the feet were () to aalyze the factors of the patiet () decisio of complicatios ad featured primary care patiet. Therefore, the objective of decisio support system is oe of the most importat public health i Thailad. The diabetic patiet decisio support system for public health is called PDSS. This uses web applicatio techology to developed by usig Laguage PHP as the mai Laguage ad usig mathematical model i.e., liear tred of algorithm Expoetial, Liear Regressio, Logarithm, Polyomial ad Power. This program collects patiet data to aalyze the risk of complicatios. The paper also discusses mathematical models used i PDSS to obtai the risk for decisio support system by providig data aalysis. Evaluatio results idicate that PDSS ca provide more tha 8% accuracy compare with real patiet data. Oe methodology of decisio support system is called program collected patiet data for aalyze the risk of complicatios. Keywords: Patiet Diabetes, Decisio Support System, Mathematical model, Risk score model.. Itroductio Sice, years, the risk for diabetes to with compared the geeral populatio. The case study of reportig for patiets with serious complicatios. The relatioship is to evirometal that causes importat for complicatios, i.e., resistace microalbumiuria, diabetic europathy, Isuli while it will be storage detailed explaatio order by this developmet required data protectio strategy that is appropriated for complicatios [, 8]. Ipatiet diabetes maagemet is becomig more importat i recet years. Give the evericreasig rates of diabetes icidece i the geeral populatio, it is o surprise that 3 percet of the patiets admitted to the hospital have a diagosis of diabetes [8]. Therefore, the treatmet of diabetic patiet is becomig a more importat aspect of ipatiet care. Although it was show that prevetio of reduces morbidity, mortality, ad legth of stay, optimal ipatiet glycemic goals remai cotroversial [6, 7]. The umber of older patiets cosultig for diabetes who also exhibit cogitive difficulties is cosistetly growig because of the icreased logevity of the populatio as a whole ad, accordig to a umber of studies, the icreased risk of cogitive impairmet ad demetia i older diabetic patiet. May studies demostrate a lik betwee poor glucose cotrol ad deteriorated cogitive fuctio i diabetic patiet. The history of severe hypoglycemic episodes is also associated with a greater risk of lateilife cogitive deficits [4]. The model for aalysis of risk diabetic patiet with persistet symptoms of foot complicatios. There are estimatig the risk of developig foot complicatios. I patiets with diabetes, high blood sugar levels i the state. The format of the existig data. The result is Glucose i brigig out the form ad cotiuity, as well as other factors such as the loss experiece of work eviromet factors. Because the respose time of the characteristics of the failure for isuli resistace [4, 5]. Curretly, most of the diabetic patiet ca be used to iform to support their decisio i selectig the patiet that cofirm to their preferece. The most commo method is the decisio support system for experieces. As discussed above, preset with the complicatios of may factors i order to obtai the aalyze risk score uder the cocept of compare. Therefore the iformatio techology ca be very helpful to support aalyze the risk of complicatios. The decisio support system is oe of iformatio techologies which help to store ad maage the DOI:.79/iciae The Istitute of Idustrial Applicatios Egieers, Japa.

2 diabetic patiet data to iform ad automatically geerate the risk of complicatios. However, all the existig decisio support system are ot cosidered o the risk score for cut the feet. This paper proposes the decisio support system of diabetic patiet for public health, uder the cocept of aalyze the factors of the patiet risk score ad decisio of complicatios for care patiet. The system framework will be proposed i sectio. Sectio presets methodology used to aalyze factors for diabetic patiet, called the risk score process. Sectio 3 describes the model aalyze for complicatios. Sectio 4 presets PDSS implemetatio issues ad techiques, presets the model evaluatio by comparig with mathematical model. The coclusios ad future work are preseted i sectio 5.. PDSS Framework The framework of PDSS system is depicted i Fig. cosistig of iput process ad output. The iput cosists of data ad method. There are three types of data used i the model developmet, which are user prefereces ad the patiet s database.. User Iput The user iput is a process that helps block ay access from uauthorized data from differet users i.e., doctor ad urse. It uses specially webcam to diagosis idividual patiet. The webcam ca sap shot the feets ad edit picture of each patiet i.e.,(add, delete, edit iput data). Moreover, it also provides differet view for authorized users. Mathematical model is a process of plaig error for diabetic patiet. While user maagemet is risk score aalyzer model ad average score. The whole process is the process of applyig kowledge for aalysis, plaig ad evaluatig the model.. Patiet Database While patiet database is used to store data of patiet i.e., factors, moth, year ad risk score to developmet of usig MySQL database ad data type i.e., Iterger, Varchar, Text, Date ad Decimal etc. They are used to store importat score levels of risk to separate factor of patiets as well as i table is equal to risk score ormal is equal to risk score low is equal to risk score medium 3 is equal to risk score high The risk score level of factors requires a advice from a expert; doctor or urse to collect data. Therefore, the expert ca list all 9 factors of diabetic patiet o database. Figure. PDSS framework This proposed system is a iteretbased applicatio cosistig of three processes as follows: Mathematical Model, Riskscore Model ad Evaluatio model. The user prefereces are data defied by user, e.g., factor for diabetic patiet of the feet that users aalyzed pla over all factors. By default, all the patiets (x) are 3 sample ad year 8..3 Mathematical Model The mathematical model were to liear programmig as regressive equatios. There are techiques learig to structured data i.e., The exploratio ad aalysis of large quatities of data i order to discover meaigful patters ad rules []. Therefore, the mathematical model is used to evaluate model for idicates that i average case of error. For example: Y = mx (i) + b; where m is a slope [3]. Where x (i) is factors, e.g., detectio of foot ulcers ad Results of the right etc. The b is a moth ad year. A example : Y = ml(x) + b ; where m is a slope. Where L(x) is iterceptio ad factors all, e.g., detectio of foot ulcers ad Results of the right etc. The b is moth ad year. The evaluatio factors are calculated from several mathematical model i table. 69

3 Table. Idfactor ad Namefactor of all diabetic patiet Id factor of the feet ito database. Namefactor detectio of the left foot detectio of foot ulcers Historical cut the legs / feet The ail problems The foot deformities Touchig the feet warm The ski color detectio of ifarctio detectio of ifarctio Posterior tibial pulse to the left Posterior tibial pulse to the right Dorsalis pedis pulse to the left Dorsalis pedis pulse to the right. (c i ) Id (c i ) Namefactor 3 4 factor Table. Mathematical Model Liear Regressio Liear Logarithmic Polyomial Power Expoetial.4 Riskscore Model Results of the right foot History of foot ulcers History of loss of cosciousess Detected wart, cor Detected hair loss. The detectio of foot fugus Results of audits sese Mathematical Model Y = mx + b Y = ml(x) + b Y = c x + c x + b Y = cx b Y = ce bx The risk score model process to equatios of average while the patiet database stores specific details of each patiet, e.g., moth per year of each member (y i ), (m i ), where 8 y i ad is the umber of all factors; Step : It stores patiet data ito database usig the risk score from the expert. The level score set of umber factor (x i ) betwee x i records all 3 member (i.e., detectio of foot ulcers, 3 ad iput data is to each patiet i detectio of the left foot, Historical cut the legs / feet etc.) Step : The create is user iterface for iput data each factors ad each patiet. It is a value used to specify by user that checkig i radio box all factors. The user ca be used to select ito each factor from over all. After the selectio, it should be submitted ito the database because of, the system of process i the time. The user eeds to cacel the process if ca t iput database. Step 3: Before submittig iput data. The iput data will be used for plaig the data of all factors which will brig the accuracy ad error data. After the user isert ito database, it ca be prefereces factors process plaig to risk score. Thus the risk score level ca be show i the output model. Step 4: Read model score that cotais factor which was selected by user. To calculate the model, the results will be read i textfile by usig file surame, /.txt. Step 5: The equatio of each factor is defied i () ad the average equatio is defied i (), risk score model is able to rak by usig these score level, i.e., ormallowermediumhigh. X(i) = w a + w a + w 3 a 3 + w 4 a 4 + w 5 a 5 + w () where X(i) is a sum all factors ad total. It is factors (w i ). cross umber patiets each idividual. risk score = x(i) i= where risk score is a average. The complex equatios, it is cout all factors (c i ) ad total ( x(i) ) derived cout all factors (c i ). Thus, risk score of threes level is defied: Where risk score betwee x i 3; risk score < ; Commet the risk low risk score <.5 ; Commet the risk lower ad medium risk score < ; Commet the risk over medium ad high The proposed risk score model for user illustrated color to (gree, yellow ad red) ad care the feets for diabetic patiet show i Figure 3. i= c i () 6

4 .5 Evaluatio Model The evaluatio model uses average while PDSS ca evaluate the model by usig 3 diabetic patiets data. Based o the mathematical model ad error (%). Thus, usig to equatios for error as defied i (3) is show optimal i table 3: error(%) = i= x(i) * (3) f i where f i is a sum all factors i 8 of estimatio. 3. PDSS Implemetatio Issues Figure 3. Compare risk score model PDSS is desiged for doctor ad urse for public health i Thailad. Thus, users ca aalyze ad plaig, risk of complicatios for each idividuals. PDSS visualize the estimatio pla results with lie chart. They are implemeted with PHP, AJAX, JavaScript, HTML ad other related web techologies. Figure 4. Output lie chart of estimated Figure 4 presets lie chart of estimated view by threes year. The user ca estimate patiet s data. They ca also calculate data of all by doctor or urse. The user ca also specify data of each patiet, compare data from real i risk score. Therefore, factors estimatio lie chart occur real compare with last threes year ago. 4. PDSS Evaluatios Figure. User Iput for diagosis patiet Figure illustrated a example graphic user iterface is iput data for diagosis patiet. They ca add, edit, update or delete parameters of each factor. The iterface is desiged as radio box, dropdow lists or textboxes with values checkers i Figure as couter level risk score for data iput The stored data will be used later i the aalysis o step ad pullig showed data. The PDSS compute appropriate factors for optimal. Examples of summary report ad Figure 4 presets output lie chart of estimated values view by group of patiets. PDSS evaluatios is coducted with obtaied from a decisio support system for of diabetic patiet for public health data (PDSS) of a 3member for public health at Poeklag Nakhorratchasima district i Thailad. The data is collected betwee year 8 to. Table 3 presets the evaluatio model. It idicates that i average case, PDSS ca perform more tha 8% accuracy (less tha 3% of error) based o the logarithm regressio methods. 6

5 Table 3. PDSS Evaluatio Model Factors Liear Logarithm Polyomial Power Expoetial Optimal detectio of foot ulcers detectio of the left foot Historical cut the legs / feet The ail problems The foot deformities Touchig the feet warm a Liear a Liear... a. Logarithm... a. Logarithm a Liear a Liear The ski color a Liear detectio of ifarctio Posterior tibial pulse to the left Posterior tibial pulse to the right Dorsalis pedis pulse to the left... a. Logarithm... a. Liear... a. Logarithm... a. Logarithm Dorsalis pedis pulse to the right a Liear Results of the right foot a Liear History of foot ulcers a Liear History of loss of cosciousess Detected wart, cor Detected hair loss. The detectio of foot fugus a Liear... a. Liear a Liear... a. Liear Results of audits sese a Power 5. Coclusios ad Future Work This paper presets a desig ad implemetatio of a decisio support system for Public Health called PDSS. The PDSS has potetial to facilitate for doctor ad urse i their decisio by providig risk score model aalysis. It supports both idividual ad the public health. The paper also discusses models based o regressio techiques used i PDSS to obtai the risk complicatios to feets of patiet. The model evaluatio results idicate that PDSS ca provides more tha 8% accuracy compare with diagosis the feets patiet. There were to optimal group i liear ad logarithm regressio. There are some improvemet that could be doe i ear future. The forecastig model used i PDSS ca be exteded with alterative forecastig methodologies such as eural etworks. The PDSS should be deployed i public health ad perform usability ad reliability testig. Refereces () Dagsoo S.kim ad James P.walsh.: Modellig Irregular Samples for Aalyzig the Risk of Complicatios of Diabets Mellitus, Vol. 8, No. 6, pp ,. () Eakasit Pacharawogsakda. : A Itroductio to Data Miig Techiques, pp.7, 5. (3) G. B. Datzig, Liear Programmig, Vol. 5, No., pp. 447,. (4) L. Bordier, J.Doucet et.al, : Update o cogitive declie ad demetia i elderly patiets with diabetes, Vol. 4, No. 5, pp , 4. (5) Rajesh Rajedra, ad Gerry Rsyma : Glycaemic maagemet i patiets with diabetes i hospital, pp. 787, 4. (6) V. Tsimihodimos, ad M. Floreti. : Hypertesio ad Dyslipidemia i Patiets with PreDiabetes: Dietary ad Other Therapies, pp. 5779, 5. (7) Swapil N. Rajpathak, Srii Rajgopala et.al., : Impact of time to treatmet itesificatio o glycemic goal attaimet amog patiets with type diabetes failig metformi mootherapy, Vol. 8, No. 6, pp , 4. (8) Zijia Che, ad Michael A. Via: Nutritioal Support i Hospitalized Patiets with Diabetes Mellitus, pp , 5. 6