Food Clustering For Diabetes Diet Health And Social Care Essay

Food bunch uping For Diabetes Diet Health And Social contend EssayThe common way for Diabetes Educators to inform diabetes patients of their nutrition therapy is by introducing sustenance substitution. The existing categorization mechanism is not efficiently for classify the nutrient for diabetic patient. Clustering info Mining (DM) Techniques empennage be a very effectual tool to collect viands items with the aforesaid(prenominal) elements into roots. This authorship looks at the use of K-mean to Cluster food info mark into groups based on food elements use RapidMiner tool .The output from the thump algorithm impart jock other recommendation systems softw atomic scrap 18 to provide patient with a effectual recommendation for there diabetes regimen.Keywordsselective information excavation diabetes, information case-hardened ,K-meant.1. basisFood and nutrition be a key to countenance trusty wellness. They are important for everyone to maintain a healthy diet especi ally for diabetic patients who have several limitations. Nutrition therapy is a major resolving power to prevent, manage and control diabetes by managing the nutrition based on the imprint that food provides vital medicine and maintains a good health. Typically, diabetic patients pack to avoid additional sugar and fat for finding the substitution from the same food group 4.The effective clustering from the various actual nutrients is undeniable to apply. The clustering entrust encourage diabetics to eat the widest possible variety of permitted food to ensure getting the full range of trace elements and other nutrients. This newspaper publisher is set out as follows. naval division 2, introduces some related pass of data archeological site and diabetic diet. Section 3, describes the utilise data set and summarize the main features that it rents. information preparation process is presented in Section 4. Section 5, describes the materials and methods used in this study. In Section 6, the conclusion is given.2. literature ReviewLi et al 1, this study proposed an automated food ontology constructed for diabetes diet care. The methods embarrass generating an ontology skeleton with hierarchical clustering algorithms (HCA)also it is used intersection appellation for class naming and instance be by granular ranking and positioning .This study based on dataset from food nutrition musical theme database of the incision Of Health the dataset. Phanich et al 2, proposed Food Recommendation System (FRS) by using food clustering analysis for diabetic patients. The system will recommend the proper substitutedfoods in the context of nutrition and food characteristic. They used Self-Organizing Map (SOM) and K-mean clustering for food clustering analysis which is based on the similarity of eight significant nutrients for diabetic patient. This study is based on the dataset Nutritive values for Thai food provided by Nutrition Division, Department of Hea lth, Ministry of Public Health (Thailand).3. entropyset DescriptionThis study is based on the dataset provided by The USDA National alimentary infobase for Standard Reference (SR)3.the Values in the database based on the results of laboratory analyses or calculated by using appropriate algorithms, factors, or recipes, as indicated by the source in the Nutrient information file. Not every food item contains a slay nutrient profile. The used data set is an abbreviated file with fewer nutrients but all the food items was included. The Dataset contains all the food items with nutrients with 7540 records and 52 charges. confuse1, 2 and 3 show data set attributes and their rendering. In order to crack up for absent value I used Rapid Miner tool. Table 4 present sample of data set.4. Data PreparationThe part of the results of the mining process is directly proportional to the quality of the data. I have first to prepare the data set by applying Data preprocessing strategies. Dat a preprocessing is an important and critical step in the data mining process, and it has a huge impact on the success of a data mining project. The purpose of data preprocessing is to cleanse the dirty/noise data. Fig. 1 shows the diametric strategies in the data preprocessing phase. In this study I focused on data alter and data reduction. check 1 strategies in data preprocessingTable 1 description of data set attributes from 1- 24Table 2 description of data set attributes from 25-48Table 3 description of data set attributes from 49-52Table 4 Sample of datasetShrt_DescWaterEnerg_KcalProteinLipid_TotAshCarbohydrtSugar_TotothersBUTTER,WITH flavour15.877170.8581.112.110.060.06BUTTER,WHIPPED,WITH SALT15.877170.8581.112.110.060.06BUTTER OIL,ANHYDROUS0.248760.2899.48000CHEESE,BLUE42.4135321.428.745.112.340.5CHEESE,BRICK41.1137123.2429.683.182.790.51Data CleaningData cleaning, also called data cleansing or scrubbing, deals with detecting and removing errors and Inconsistencies from dat a in order to improve the quality of data 6. The aim of data cleaning is to raise the data quality to a level suitable for the clustering analyses. The Methods used for data cleaning are fill in missing values and eliminate data redundancy.Missing valueIt is common for the dataset to have fields that contain unknown or missing values. There are a variety of legitimate reasons why this can happen. There are a number of methods for treating records that contain missing values 71. Omit the absurd field(s)2. Omit the entire record that contains the in train field(s)3. Automatically enter/correct the data with default values e.g. select the mean from the range4. understand a model to enter/correct the data5. Replace all values with a global constantWithin this study both missing and unknown data have been set to zero.Duplicated RecordsDuplicate records do not share a common key and/or they contain errors that make duplicate matching a difficult task. Errors are introduced as the result of transcription errors, incomplete information, lack of standard formats, or both combination of these factors 7 . The data set used in this study include data objects that are duplicate. Using RapidMiner to removing duplication .As result from this process the 7540 records fall to 7139 record.Data ReductionData reduction can be achieved in some(prenominal) ways one way is by selecting features 5, The used data set contains many Irrelevant features that contain almost no useful information for data mining task As 2 I will focus only on eight attributes out of fifty both attributes, as they are important for diabetes diet.The eight nutrients includeCarbohydrate zilchFatproteinFibervitamin EVitamin B1(also known as thiamine)Vitamin CData NormalizationData normalization is one of the preprocessing procedures in data mining, where the attribute data are scaled so as to fall inwardly a small specified range such(prenominal) as -1.0 to 1.0 or 0.0 to 1.0.Normalization before cluster ing is specially needed for distance metric, such as Euclidian distance, which are sensitive to differences in the magnitude or scales of the attributes.The K-Means typically uses Euclidean distance to measure the distortion between a data object and its cluster centroid .However, the clustering results can be greatly affected by differences in scale among the dimension from, which the distances are computed. Data normalization is the linear transformation of data to a specific range. Therefore, it is worthy to enhance clustering quality by normalizing the dynamic range of input data objects into specific range 8.in this study I will season data to the range of 0, 1 . Figure 2 show the result from the data preprocessingFigure 2 Result from Preprocessing(Data cleaning , Data Reduction , Data Normalization)5. Data Analysis MethodologyAfter data preparation, a secondment step is using a K-means to cluster food data set. In order to work with optimal k-value as 2 used the Davies-Bou ldin index 9 to measure out the optimal k-value. The k-value is optimal when the related index is smallest. For this study,I used K=19 since it gives the smallest value.The final result is the food clusters which foods in the same group provide the approximate amount of the eight nutrients. Data analysis solution RapidMiner was used to analysis the data set and cluster food item. The unanimous process sequence shown in figure 3.figure 4, 5, 6 shows the final result.Figure 3 data analysis processFigure4 food Items clustered into 19 clustersFigure4 distribution of 8 Nutrients into clusters from (0-12)Figure4 distribution of 8 Nutrients into clusters from (13-18)5.1 K-mean Evaluationa execution based on the number of clusters.This operation builds a derived index from the number of clusters by using the formula 1 (k / n) with k number of clusters and n covered examples. It is used for optimizing the coverage of a cluster result in respect to the number of clusters. By applying the K-mean model to this data set the Cluster number index = 0.997 witch indicate a good coverage.6. evidenceData mining has been widely used in many health care fields. The Diabetes Diet Care was one of the health problems that data mining play role on it .this experiment are conducted based on USDA National Nutrient dataset. The results demonstrate that K-mean is very effective and it can successfully create food groups that will help in many recommendations systems.