Evaluation of Different Machine Learning Methods for Caesarean Data Classification

Alsharif, O. S. S.; Elbayoudi, K. M.; Aldrawi, A. A. S.; Akyol, K.
September 2019
International Journal of Information Engineering & Electronic Bu;Sep2019, Vol. 11 Issue 5, p19
Academic Journal
Recently, a new dataset has been introduced about the caesarean data. In this paper, the caesarean data was classified with five different algorithms; Support Vector Machine, K Nearest Neighbours, Naïve Bayes, Decision Tree Classifier, and Random Forest Classifier. The dataset is retrieved from California University website. The main objective of this study is to compare selected algorithms' performances. This study has shown that the best accuracy that was for Naïve Bayes while the highest sensitivity which was for Support Vector Machine.


Related Articles

  • Evaluation of SVM Kernels and Conventional Machine Learning Algorithms for Speaker Identification. Mezghani, D. Ben Ayed; Boujelbene, S. Zribi; Ellouze, N. // International Journal of Hybrid Information Technology;2010, Vol. 3 Issue 3, p23 

    One of the central problems in the study of Support vector machine (SVM) is kernel selection, that's based essentially on the problem of choosing a kernel function for a particular task and dataset. By contradiction to other machine learning algorithms, SVM focuses on maximizing the...

  • Sentiment Analyzer for Arabic Comments System. Hamouda, Alaa El-Dine Ali; El-taher, Fatma El-zahraa // International Journal of Advanced Computer Science & Application;Mar2013, Vol. 4 Issue 3, p99 

    Today, the number of users of social network is increasing. Millions of users share opinions on different aspects of life every day. Therefore social network are rich sources of data for opinion mining and sentiment analysis. Also users have become more interested in following news pages on...

  • THE EFFECTIVENESS OF AUTOMATED THAI DOCUMENTS CATEGORIZATION BASED ON MACHINE LEARNING. JANPLA, SATIEN // Journal of Theoretical & Applied Information Technology;8/10/2014, Vol. 66 Issue 1, p43 

    The purpose of this research was to test the effectiveness of the Thai language document categorization model by using the features of reduction and machine learning techniques. The experimental results showed that the support vector machine algorithm used to classify Thai documents did so with...

  • A Framework for Semantic Interpretation of Noun Compounds Using Tratz Model and Binary Features. Zaeri, Ahmad; Nematbakhsh, Mohammad Ali // ETRI Journal;Oct2012, Vol. 34 Issue 5, p743 

    Semantic interpretation of the relationship between noun compound (NC) elements has been a challenging issue due to the lack of contextual information, the unbounded number of combinations, and the absence of a universally accepted system for the categorization. The current models require a huge...

  • OVA Tree Multiclass Framework for Support Vector Machine. Sidaoui, Boutkhil; Sadouni, Kaddour // Proceedings of the International MultiConference of Engineers & ;2013, p1 

    In this paper we propose and examine the performance of a framework for solving multiclass problems with Support Vector 1Machine (SVM). Our methods based on the principle binary tree, leading to much faster convergence and compare it with very popular methods proposals in the literature, both in...

  • Distinguishing Fraud from Error in Restatement Data Using Machine Learning Techniques. Mei Zhang; Haibin Ling // Academy of Business Journal;Fall2013, Vol. 2, p1 

    In this paper, we propose using machine learning techniques to distinguish errors from frauds. We treat the task as a binary classification problem, i.e., mapping from an input vector of indices to a class label of either error or fraud. We empirically evaluate and analyze five state-of-the-art...

  • Electronic Nose Odor Classification with Advanced Decision Tree Structures. GÜNEY, Selda; ATASOY, Ayten; BURGET, Radim // Radioengineering;Sep2013, Vol. 22 Issue 3, p874 

    Electronic nose (e-nose) is an electronic device which can measure chemical compounds in air and consequently classify different odors. In this paper, an e-nose device consisting of 8 different gas sensors was designed and constructed. Using this device, 104 different experiments involving 11...

  • Classification of P2P traffic based on a heteromorphic ensemble learning model. Li Ding; Li Mao; XiaoFeng Wang // Applied Mechanics & Materials;2014, Vol. 687-691, p2693 

    One single machine learning algorithm presents shortcomings when the data environment changes in the process of application. This article puts forward a heteromorphic ensemble learning model made up of bayes, support vector machine (SVM) and decision tree which classifies P2P traffic by voting...

  • A study on the classification ability of decision tree and support vector machine in gearbox fault detection. Saimurugan, M.; Praveenkumar, T.; Krishnakumar, P.; Ramachandran, K. I. // Applied Mechanics & Materials;2015, Vol. 813/814, p1058 

    Gearbox is the only medium which balances the power and torque relations for the appropriate operating conditions, at very high speeds it controls the power output of the drive unit. Its application is wide in the field of automotive and industries. Condition monitoring of gearbox access the...


Read the Article


Sorry, but this item is not currently available from your library.

Try another library?
Sign out of this library

Other Topics