Expert System for Diagnosing Early Childhood Developmental Disorders with Certainty Factor Method

Nandang Hermanto, Zulfiqar Shertian Ramadhan


The purpose of this research is to design and build an expert system to diagnose the type of developmental disorder in children early using the certainty factor method. The method of data collection used in this study is observation, interviews, and library studies. The system was built with the Waterfall System Development Method. The stage of the Waterfall method is analysis, design, coding, and testing. This expert system is built using the PHP programming language and MySQL database. The result of this research was to successfully build an expert system to diagnose the type of developmental disorder in children early using the Certainty factor method to facilitate the user in diagnosing developmental disorders in the child quickly, efficiently, and without having to consult a pediatrician.

Article Metrics

Abstract: 204 Viewers PDF: 130 Viewers


Expert system; Certainty factor; Children; Waterfall; Website

Full Text:



Charte F, Rivera AJ, del Jes ́ us MJ, Herrera F (2015) Addressing imbalance inmultilabel classification: Measures and random resampling algorithms. Neurocomputing 163:3–16

Cheng W, Hullermeier E (2009) Combining instance-based learning andlogistic regression for multi-label classification. Machine Learning 76(2-3):211–225

Clare A, King RD (2001) Knowledge discovery in multi-label phenotypedata. In: PKDD’01, Springer, pp 42–53

Doquire G, Verleysen M (2013) Mutual information-based feature selectionfor multilabel classification. Neurocomputing 122:148–155

Duda RO, Hart PE, Stork DG (2001) Pattern Classification, 2nd edn. NewYork: Wiley

Furnkranz J, Hullermeier E, Mencia EL, Brinker K (2008) Multilabel classification via calibrated label ranking. Machine Learning 73(2):133–153

Gibaja E, Ventura S (2015) A tutorial on multi-label learning. ACM Computing Surveys 47(3):1–38

Hullermeier E, Furnkranz J, Cheng W, Brinker K (2008) Label ranking bylearning pairwise preferences. Artificial Intelligence 172(16-17):1897–1916

Kuncheva LI, Rodriguez JJ (2014) A weighted voting framework for classifiers ensembles. Knowledge and Information Systems 38(2):259–275

Li P, Li H, Wu M (2013) Multi-label ensemble based on variable pairwiseconstraint projection. Information Sciences 222:269–281

Liu H, Zhang S, Wu X (2014) Mlslr: Multilabel learning via sparse logisticregression. Information Sciences 281:310–320

Liu H, Ma Z, Zhang S, Wu X (2015) Penalized partial least square discriminant analysis with1-norm for multi-label data. Pattern Recognition48(5):1724–1733

Ma HP, Chen EH, Xu LL, Xiong H (2012) Capturing correlations of multiplelabels: A generative probabilistic model for multi-label learning. Neurocomputing 92:116–123

Montanes E, Senge R, Barranquero J, Ramon Quevedo J, Jose del Coz J, Huellermeier E (2014) Dependent binary relevance models for multi-labelclassification. Pattern Recognition 47(3):1494–1508

Read J, Pfahringer B, Holmes G, Frank E (2011) Classifier chains for multilabel classification. Machine Learning 85(3):335–359

Reyes OG, Morell C, Ventura S (2015) Scalable extensions of the relieffalgorithm for weighting and selecting features on the multi-label learning context.Neurocomputing 161:168–182


  • There are currently no refbacks.

barcodeInternational Journal of Informatics and Information Systems (IJIIS)
ISSN: 2579-7069 (online)
Organized by Information System Department - Universitas Amikom Purwokerto - Indonesia, Laboratoire Signaux Et Systèmes (L2s) - Université Paris 13 - France, and Bright Publisher
Published by Bright Publisher
Website :
Email :,

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0