Abstract
Data mining is an important research activity in the field of medical sciences since there is a requirement of efficient methodologies for analyzing and detecting diseases. Data mining applications are used for the management of healthcare, health information, patient care system, etc. It also plays a major role in analyzing survivability of a disease. Classification and clustering are the popular data mining techniques used to understand the various parameters of the health data set. In this research work, various classification models are used to classify thyroid disease based on the parameters like TSH, T4U and goiter. Several classification techniques like K-nearest neighbour, support vector machine and Naive Bayes are used. The experimental study has been conducted using Rapid miner tool and the results shows that the accuracy of K-nearest neighbour is better than Naive Bayes to detect thyroid disease.
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Sehgal MSB, Gondal I (2014) K-ranked covariance based missing values estimation for microarray data classification. In: IEEE, 2004
Bonner A (2004) Comparison of discrimination methods for peptide classification in tandem mass spectrometry. In: IEEE, 2004
Shen X, Lin Y (2004) Gene expression data classification using SVM–KNN classifier”. In: IEEE, 2004
Xia C, Hsu W (2006) BORDER: efficient computation of boundary points. In: IEEE, 2006
Karimifard S, Ahmadian A (2006) Morphological heart arrhythmia detection using hermitian basis functions and kNN classifier. In: IEEE, 2006
Aslandogan YA, Mahajani GA (2007) Evidence combination in medical data mining. In: IEEE, 2007
Yu H, Gu G (2008) Incremental tumor diagnosis algorithm using new unlabeled microarray. In: IEEE, 2008
Qing C, Qingfeng W (2009) The research of missing value estimation of gene sequence based on improved KNN. In: IEEE, 2009
Vepa J (2009) Classification of heart murmurs using cepstral features and support vector machines. In: IEEE, 2009
Christodoulou CI (2009) Classification of surface electromyographic signals using AM–FM features. In: IEEE, 2009
Liangliang S, Nian W (2010) The classification of gene expression profile based on the adjacency matrix spectral decomposition. In: IEEE, 2010
Chan J, Leung H (2010) Correlation among joint motions allows classification of parkinsonian versus normal 3-D reaching. In: IEEE, 2010
Shirvan RA, Tahami E (2011) Voice analysis for detecting Parkinson’s disease using genetic algorithm and KNN classification method. In: IEEE, 2011
Wisittipanit N (2011) Analysis of microbiome data across inflammatory bowel disease patients. In: IEEE, 2011
Alizadehsani R, Hosseini MJ, Sani ZA (2012) Diagnosis of coronary artery disease using cost-sensitive algorithms. In: IEEE, 2012
Hudli SA, Hudli AV, Hudli AA (2012) Application of data mining to candidate screening. In: 2012 IEEE international conference on advanced communication control and computing technologies (ICACCCT). IEEE, pp 287–290
Lan Y, Ren H (2012) A hybrid classifier for mammography CAD. In: IEEE, 2012
Guo D, Li J (2012) Research on optimal traditional chinese medicine treatment of knee ostarthritis with data mining algorithms. In: IEEE, 2012
NirmalaDevi M, Alias Balamurugan SA, Swathi UV (2013) An amalgam KNN to predict diabetes mellitus. In: IEEE, 2013
Siirtola P, Pyky R (2014) Detecting and profiling sedentary young men using machine learning algorithms. In: IEEE, 2014
Gulhane VA (2014) Diagnosis of diseases on cotton leaves using principal component analysis classifier. In: IEEE, 2014
Alivar A, Daniali H (2014) Classification of liver diseases using ultrasound images based on feature combination. In: IEEE, 2014
Roychowdhury S (2014) DREAM: diabetic retinopathy analysis using machine learning. In: IEEE, 2014
Simonthomas S (2014) Automated diagnosis of glaucoma using haralick texture features. In: IEEE, 2014
Chetty N, Vaisla KS, Patil N (2015) An improved method for disease prediction using fuzzy approach. In: IEEE, 2015
Dai Y, Ru B (2015) Feature selection of high-dimensional biomedical data using improved SFLA for disease diagnosis. In: IEEE, 2015
Lotfi M, Nazari B (2015) The detection of dacrocyte, schistocyte and elliptocyte cells in iron deficiency anemia. In: IEEE, 2015
Tiwari AK (2015) Feature based classification of nuclear receptors and their subfamilies using fuzzy K-nearest neighbour. In: IEEE, 2015
Munla N, Khalil M, Shahin A, Mourad A (2015) Driver stress level detection using HRV analysis. In: 2015 international conference on advances in biomedical engineering (ICABME). IEEE, pp 61–64
Lachure J, Deorankar AV (2015) Diabetic retinopathy using morphological operations and machine learning. In: IEEE, 2015
Heldberg BE, Kautz T (2015) Using wearable sensors for semiology-independent seizure detection—towards ambulatory monitoring of epilepsy. In: IEEE, 2015
Babu MSP (2015) Artificial immune recognition systems in medical diagnosis. In: IEEE, 2015
Kalbkhani H, Salimi A, Mahrokh MG (2015) Classification of brain MRI using multi-cluster feature selection and KNN classifier. In: IEEE, 2015
Niazi KAK (2015) Identifying best feature subset for cardiac arrhythmia classification. In: IEEE, 2015
Saini R, Bindal N (2015) Classification of heart diseases from ECG signals using wavelet transform and kNN classifier. In: IEEE, 2015
Caesarendra W (2015) Pattern recognition methods for multi stage classification of Parkinson’s disease utilizing voice features. In: IEEE, 2015
Alpalsan N, Kara A (2015) Classification of breast masses in mammogram images using KNN. In: IEEE, 2015
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Chandel, K., Kunwar, V., Sabitha, S. et al. A comparative study on thyroid disease detection using K-nearest neighbor and Naive Bayes classification techniques. CSIT 4, 313–319 (2016). https://doi.org/10.1007/s40012-016-0100-5
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DOI: https://doi.org/10.1007/s40012-016-0100-5