Identification of Toddlers’ Nutritional Status using Data Mining Approach

One of the problems in community health center or health clinic is documenting the toddlers’ data. The numbers of malnutrition cases in developing country are quite high. If the problem of malnutrition is not resolved, it can disrupt the country’s economic development. This study identifies malnutrition status of toddlers based on the context data from community health center (PUSKESMAS) in Jogjakarta, Indonesia. Currently, the patients’ data cannot directly map into appropriate groups of toddlers’ malnutrition status. Therefore, data mining concept with k-means clustering is used to map the data into several malnutrition status categories. The aim of this study is building software that can be used to assist the Indonesian government in making decisions to take preventive action against malnutrition.

[1]  Dayou Li,et al.  Visualising Arabic Sentiments and Association Rules in Financial Text , 2017 .

[2]  Sri Kusumadewi,et al.  Determining the nutrition of patient based on food packaging product using fuzzy C means algorithm , 2017, 2017 4th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI).

[3]  Abdul Aziz,et al.  Detection of Cardiac Disease using Data Mining Classification Techniques , 2017 .

[4]  Dolluck Phongphanich,et al.  Basic Health Screening by Exploiting Data Mining Techniques , 2017 .

[5]  Zura Kakushadze,et al.  *K-means and cluster models for cancer signatures , 2017, Biomolecular detection and quantification.

[6]  R. Johnston,et al.  Child Care Health Consultation Improves Infant and Toddler Care. , 2017, Journal of pediatric health care : official publication of National Association of Pediatric Nurse Associates & Practitioners.

[7]  Margaretha Ohyver,et al.  Logistic Regression and Growth Charts to Determine Children Nutritional and Stunting Status: A Review , 2017, ICCSCI.

[8]  D. Thangamani,et al.  Identification of Various Deficiencies Using Data Mining Techniques - A Survey , 2014 .

[9]  Ahmad Azhari,et al.  Brainwaves feature classification by applying K-Means clustering using single-sensor EEG , 2016 .

[10]  Iwan Tri Riyadi Yanto,et al.  Histogram Thresholding for Automatic Color Segmentation Based on k-means Clustering , 2016, SCDM.

[11]  Sudha,et al.  Appraising Research Direction & Effectiveness of Existing Clustering Algorithm for Medical Data , 2017 .

[12]  Lei Chen,et al.  Identification of new candidate drugs for lung cancer using chemical–chemical interactions, chemical–protein interactions and a K-means clustering algorithm , 2016, Journal of biomolecular structure & dynamics.

[13]  Mariofanna Milanova,et al.  Classification of Alzheimer Disease based on Normalized Hu Moment Invariants and Multiclassifier , 2017 .

[14]  Bharat Tidke,et al.  Improved K-means Algorithm for Searching Research Papers , 2014 .

[15]  Shireen Panchoo,et al.  K-means clustering based filter feature selection on high dimensional data , 2016 .

[16]  P. M. Ameer,et al.  An enhanced deterministic K-Means clustering algorithm for cancer subtype prediction from gene expression data , 2017, Comput. Biol. Medicine.

[17]  Li-Hong Juang,et al.  MRI brain lesion image detection based on color-converted K-means clustering segmentation , 2010 .

[18]  Kyungtae Kang,et al.  Recommendations for antiarrhythmic drugs based on latent semantic analysis with fc-means clustering , 2016, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[19]  H. B. Riley,et al.  Drowsy Driving Detection by EEG Analysis Using Wavelet Transform and K-means Clustering , 2014, FNC/MobiSPC.

[20]  Ivan Lukovic,et al.  Using association rule mining to identify risk factors for early childhood caries , 2015, Comput. Methods Programs Biomed..

[21]  Sung-il Cho,et al.  Employment status and depressive symptoms in Koreans: results from a baseline survey of the Korean Longitudinal Study of Aging. , 2009, The journals of gerontology. Series B, Psychological sciences and social sciences.

[22]  Herman Yuliansyah,et al.  Designing web-based data mining applications to analyze the association rules tracer study at university using a FOLD-growth method , 2016 .

[23]  J Studnicki,et al.  Comparing alternative methods for composing community peer groups: a data warehouse application. , 2001, Journal of public health management and practice : JPHMP.

[24]  P. Novianti,et al.  K-Means cluster analysis in earthquake epicenter clustering , 2017 .