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Improve the Classifier Accuracy for Continuous Attributes in Biomedical Datasets Using a New Discretization Method

G. Madhu, T.V. Rajinikanth, A. Govardhan
2014 Procedia Computer Science  
Motivation  Many real-world datasets are predominately consist of continuous attributes also called quantitative attributes.  These type of datasets are unsuitable for certain data mining algorithms  ...  continuous attributes into nominal attributes this process known as 'Discretization'.  Even though some traditional methods have disadvantages like unbalanced intervals, presence of outliers, also unsupervised  ...  Acknowledgement The author would like to thank the Associates of ITQM 2014 members for their valuable support.  ... 
doi:10.1016/j.procs.2014.05.315 fatcat:a3d6igm4bbcdlptzkvq2bzru5u

Multi-Objective Evolutionary approach for the Performance Improvement of Learners using Ensembling Feature selection and Discretization Technique on Medical data [article]

Deepak Singh, Dilip Singh Sisodia, Pradeep Singh
2020 arXiv   pre-print
In response, pre-processing techniques on dataset minimizes the side effects and have shown success in maintaining the adequate accuracy.  ...  Biomedical data is filled with continuous real values; these values in the feature set tend to create problems like underfitting, the curse of dimensionality and increase in misclassification rate because  ...  Table 5 assembles the accuracy values and the relevant improvement in the reduction rate on the standard biomedical dataset.  ... 
arXiv:2004.07478v1 fatcat:7mqsxbqmsveh5jqoeyllk7gmfu

An Optimized DiscretizationApproach using k-Means Bat Algorithm

Rozlini Mohamed
2021 Turkish Journal of Computer and Mathematics Education  
The cluster centroid is very useful to determine appropriate breakpoint for discretization. The proposed discretization approach is applied in the experiments with continuous datasets.  ...  Decision Tree, k-Nearest Neighbours and Naïve Bayes classifiers are used in the experiments.  ...  In The Proposed Discretization Approach This research proposed a new discretization approach, called for discretizing the continuous values of a datasets.  ... 
doi:10.17762/turcomat.v12i3.1013 fatcat:riuikphv2zc3fgm63io6pwl3ru

Physician-Friendly Machine Learning: A Case Study with Cardiovascular Disease Risk Prediction

Meghana Padmanabhan, Pengyu Yuan, Govind Chada, Hien Van Nguyen
2019 Journal of Clinical Medicine  
In particular, the graduate student manually builds multiple machine learning classifiers and tunes their parameters for one month using scikit-learn library, which is a popular machine learning library  ...  models and the final classification accuracies on unseen test datasets.  ...  Conflicts of Interest: The authors declare no conflict of interest. Abbreviations The following abbreviations are used in this manuscript:  ... 
doi:10.3390/jcm8071050 pmid:31323843 pmcid:PMC6678298 fatcat:bpapqfz7ejalnih7a4my6uvhja

Discretization of continuous features in clinical datasets

D. M. Maslove, T. Podchiyska, H. J. Lowe
2013 JAMIA Journal of the American Medical Informatics Association  
Also, in the 'Discretization methods' section, 'MDL discretion' has been corrected to 'MDL discretization'.  ...  In the 'Clinical datasets' section, the sentence beginning with 'All measures within a given set were taken within 15 min of each other' was incorrect. '15 min' has been corrected to '30 min'.  ...  It is also possible that methods which use a mixture of different discretization methods within a single dataset could lead to improved classification accuracy.  ... 
doi:10.1136/amiajnl-2012-000929 pmid:23059731 pmcid:PMC3628044 fatcat:chcaqg76onaepj6ux4ec5wvk4i

An Individualized Preprocessing for Medical Data Classification

Sarab AlMuhaideb, Mohamed El Bachir Menai
2016 Procedia Computer Science  
Experimental results on 25 real-world medical datasets show that a significant relative improvement in predictive accuracy, exceeding 60% in some cases, is obtained.  ...  In this paper, we show that selecting the right combination of preprocessing methods has a considerable impact on the classification potential of a dataset.  ...  discretization method fay for more than 41% in predictive accuracy, and the improvement obtained when using eib5 over the default fay is over 54% for the wpbc dataset as well.  ... 
doi:10.1016/j.procs.2016.04.006 fatcat:l6xcwmskc5g3nn6ejelt3zsgmu

Extracting Subset of Relevant Features for Breast Cancer to Improve Accuracy of Classifier

finding a better classifier for images to estimate accuracy of classification in medical image processing.  ...  Feature extraction, variable selection is a method of obtaining a subset of relevant characteristics from large dataset. Too many features of a class may affect the accuracy of classifier.  ...  This reduced data set can improve accuracy of a classifier compared with original dataset.  ... 
doi:10.35940/ijitee.k1507.0981119 fatcat:kbn3qqehlff2zkmhjb3wmjf6ru

Nanotechnology-Based Sensitive Biosensors for COVID-19 Prediction Using Fuzzy Logic Control

Vikas Maheshwari, Md Rashid Mahmood, Sumukham Sravanthi, N. Arivazhagan, A. ParimalaGandhi, K. Srihari, R. Sagayaraj, E. Udayakumar, Yuvaraj Natarajan, Prashant Bachanna, Venkatesa Prabhu Sundramurthy, Lakshmipathy R
2021 Journal of Nanomaterials  
This work uses a normalized classifier namely fuzzy-based decision tree (FDT) algorithm for classifying the data collected via nanotechnology-based IOT biosensors, and this helps in the identification  ...  The results of the simulation show that the proposed method achieves a higher rate of accuracy than the other methods.  ...  The FDT builds the decision tree using discrete procedure in which a fuzzy system is specified for a certain attribute A i : B = A i , e B A i ð Þ h iA i ∈ A j f g : ð6Þ Finally, the HI G over an attribute  ... 
doi:10.1155/2021/3383146 fatcat:fno73n454becpkj6qaoeenmude

Reliable Distributed Fuzzy Discretizer for Associative Classification of Big data

2022 International Journal of Information Retrieval Research  
Proposed discretization method is compared with existing distributed fuzzy partitioning method and achieved good accuracy in the performance of associative classifiers.  ...  Our proposed method is implemented in distributed fuzzy environment and aims to avoid over splitting of partitions by introducing a novel stopping criteria.  ...  .,(2014) proposed new non parametric discretization method ZDISC based on Z-score discretization in five biomedical datasets and compared with other discretization methods such as Ameva, Baysian, CACC  ... 
doi:10.4018/ijirr.289572 fatcat:r7hlgg6ag5eulksuexdm54t3hi

Ensemble Learning Based on Active Example Selection for Solving Imbalanced Data Problem in Biomedical Data

Min Su Lee, Sangyoon Oh, Byoung-Tak Zhang
2009 2009 IEEE International Conference on Bioinformatics and Biomedicine  
Since trained classifiers using imbalanced data are mostly derived from the majority class, their prediction performance is poor for the minority class.  ...  The imbalanced data problem is popular in biomedical classification tasks.  ...  As a data preprocessing step continues, a range of numeric attributes in the dataset is discretized into nominal attributes for naïve Bayes classifier.  ... 
doi:10.1109/bibm.2009.44 dblp:conf/bibm/LeeOZ09 fatcat:kadvbhfo25ev7drbu5wvghorkm

Graph clustering-based discretization of splitting and merging methods (GraphS and GraphM)

Kittakorn Sriwanna, Tossapon Boongoen, Natthakan Iam-On
2017 Human-Centric Computing and Information Sciences  
Moreover, discretization is also expected to improve the predictive accuracy for classification [4] and Label Ranking [5].  ...  Because real datasets are always a combination of numeric and nominal vales, for an algorithm that only takes nominal data, numerical attributes need to be discretized into nominal attributes before the  ...  Acknowledgements The authors would like to thank KEEL software [59, 60] for distributing the source code of discretization algorithms, and the authors of EMD [31] for EMD program, and the authors of  ... 
doi:10.1186/s13673-017-0103-8 fatcat:lialeoe5abf4ffvjzbnsxmi54u

Fetal distress prediction using discriminant analysis, decision tree, and artificial neural network

Mei-Ling Huang, Yung-Yan Hsu
2012 Journal of Biomedical Science and Engineering  
The results show that the accuracies of DA, DT and ANN are 82.1%, 86.36% and 97.78%, respectively.  ...  Fetal distress is one of the main factors to cesarean section in obstetrics and gynecology. If the fetus lack of oxygen in uterus, threat to the fetal health and fetal death could happen.  ...  The reason why our results shows lower classification accuracy might be we only use the continuous attributes in our models, the information implicit in discrete attributes perhaps pos-sess valuable information  ... 
doi:10.4236/jbise.2012.59065 fatcat:4zdmhclrbzec3dsbpnjihg2jta

Advanced Filter Based Machine Learning Models on Clinical Databases for Outlier Detection

Devi Satya Sri V
2021 Revista GEINTEC  
In this work, a hybrid data transformation and outlier detection methods are developed on the clinical databases to improve the classification accuracy.  ...  The T-test is another common statistical method used to compare a  ...  Using Greedy Search method and Greedy Search Loss from ranking method, this algorithm was used to find the best features. [26] Introduced a new algorithm called ReliefDisc.  ... 
doi:10.47059/revistageintec.v11i3.2003 fatcat:bbmmanspnvhkzg7kcihqo775wy

Development of the classifier based on a multilayer perceptron using genetic algorithm and cart decision tree

Lyudmila Dobrovska, Olena Nosovets
2021 Eastern-European Journal of Enterprise Technologies  
The possibility of improving the classifiers of biomedical data in the form of NN based on GA by applying the process of appropriate preparation of biomedical data using the CART decision tree has been  ...  Many studies are aimed at developing methods for analyzing these data, among them there are methods based on a neural network (NN) in the form of a multilayer perceptron (MP) using GA.  ...  The result of the functioning (modeling) of a binary classifier on reduced databases is shown in Table 7 . Accuracy shows how many correct results were obtained using a given method (Table 8 ).  ... 
doi:10.15587/1729-4061.2021.242795 fatcat:j4xqkc3bjvetvcipc6tfsxe3y4

Biomedical Image Classification in a Big Data Architecture Using Machine Learning Algorithms

Christian Tchito Tchapga, Thomas Attia Mih, Aurelle Tchagna Kouanou, Theophile Fozin Fonzin, Platini Kuetche Fogang, Brice Anicet Mezatio, Daniel Tchiotsop, Aiping Liu
2021 Journal of Healthcare Engineering  
The goal of this paper is to perform a survey of classification algorithms for biomedical images.  ...  The concept of classification in machine learning deals with the problem of identifying to which set of categories a new population belongs.  ...  Romanic Kengne and the InchTech's team (, for their support and assistance during the conception of this work.  ... 
doi:10.1155/2021/9998819 pmid:34122785 pmcid:PMC8191587 fatcat:2fu4pff7kba75km5liu752i26a
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