Classification of complete blood count and haemoglobin typing data by a C4.5 decision tree, a naïve Bayes classifier and a multilayer perceptron for thalassaemia screening

Setsirichok, Damrongrit; Piroonratana, Theera; Wongseree, Waranyu; Usavanarong, Touchpong; Paulkhaolarn, Nuttawut; Kanjanakorn, Chompunut; Sirikong, Monchan; Limwongse, Chanin; Chaiyaratana, Nachol

doi:10.1016/j.bspc.2011.03.007

Cited by 37 publications

(23 citation statements)

References 25 publications

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“…For example, the results of a neural network cannot be interpreted by a doctor or physician, yet the tree structure can easily be justified. Also, it is easy to implement and works well for large volume of data [19][20][21].…”

Section: Decision Treementioning

confidence: 98%

Comparison of various classification algorithms in the diagnosis of type 2 diabetes in Iran

Heydari

Teimouri

Heshmati

et al. 2015

Int J Diabetes Dev Ctries

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In today's medical world, data on symptoms of patients with various diseases are so widespread, that analysis and consideration of all factors is merely not possible by a person (doctor). Therefore, the need for an intelligent system to consider the various factors and identify a suitable model between the different parameters is evident. Knowledge of data mining, as the foundation of such systems, has played a vital role in the advancement of medical sciences, especially in diagnosis of various diseases. Type 2 diabetes is one of these diseases, which has increased in recent years, which if diagnosed late can lead to serious complications. In this paper, several data mining methods and algorithms have been used and applied to a set of screening data for type 2 diabetes in Tabriz, Iran. The performance of methods such as support vector machine, artificial neural network, decision tree, nearest neighbors, and Bayesian network has been compared in an effort to find the best algorithm for diagnosing this disease. Artificial neural network with an accuracy rate of 97.44 % has the best performance on the chosen dataset. Accuracy rates for support vector machine, decision tree, 5-nearest neighbor, and Bayesian network are 81.19, 95.03, 90.85, and 91.60 %, respectively. The results of the simulations show that the effectiveness of various classification techniques on a dataset depends on the application, as well as the nature and complexity of the dataset used. Moreover, it is not always possible to say that a classification technique will always have the best performance. Therefore, in cases where data mining is used for diagnosis or prediction of diseases, consultation with specialists is inevitable, for selecting the number and type of dataset parameters to obtain the best possible results.

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Section: Decision Treementioning

confidence: 98%

Comparison of various classification algorithms in the diagnosis of type 2 diabetes in Iran

Heydari

Teimouri

Heshmati

et al. 2015

Int J Diabetes Dev Ctries

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show abstract

“…Applications of machine learning techniques are becoming increasingly popular for classification of medical data. Among various classification methods, neural network based classifiers have been successfully used in a number of applications like medical image analysis [11], [17], [18].…”

Section: Related Workmentioning

confidence: 99%

“…where KL(t) is the Kalman gain and e is the error obtained from hinge loss function. The selfregulated control parameters are updated using the equations (17) and (18).…”

Section: Self Adaptive Resource Allocation Network Classifier (Sran)mentioning

confidence: 99%

Computer Aided System for Detection and Classification of Breast Cancer

Shanthi¹,

Bhaskaran²

2012

IJITCA

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Breast cancer is one of the most important causes of death among all type of cancers for grown-up and older women, mainly in developed countries, and its rate is rising. Since the cause of this disease is not yet known, early detection is the best way to decrease the breast cancer mortality. At present, early detection of breast cancer is attained by means of mammography. An intelligent computer-aided diagnosis system can be very helpful for radiologist in detecting and diagnosing cancerous cell patterns earlier and faster than typical screening programs. This paper proposes a computer aided system for automatic detection and classification of breast cancer in mammogram images. Intuitionistic Fuzzy C-Means clustering technique has been used to identify the suspicious region or the Region of Interest automatically. Then, the feature data base is designed using histogram features, Gray Level Concurrence wavelet features and wavelet energy features. Finally, the feature database is submitted to self-adaptive resource allocation network classifier for classification of mammogram image as normal, benign or malignant. The proposed system is verified with 322 mammograms from the Mammographic Image Analysis Society Database. The results show that the proposed system produces better results.

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“…These advantages are available for facial expression classification. Among many decision tree algorithms, C4.5 classifier [14] has been widely used in the field of image recognition and has the ability to classify and recognize facial expressions. Therefore, C4.5 classifier is selected as the classifier for expression recognition in this paper.…”

Section: Introductionmentioning

confidence: 99%

Facial Expression Recognition Based on Random Forest and Convolutional Neural Network

Wang

Song

et al. 2019

Information

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As an important part of emotion research, facial expression recognition is a necessary requirement in human–machine interface. Generally, a face expression recognition system includes face detection, feature extraction, and feature classification. Although great success has been made by the traditional machine learning methods, most of them have complex computational problems and lack the ability to extract comprehensive and abstract features. Deep learning-based methods can realize a higher recognition rate for facial expressions, but a large number of training samples and tuning parameters are needed, and the hardware requirement is very high. For the above problems, this paper proposes a method combining features that extracted by the convolutional neural network (CNN) with the C4.5 classifier to recognize facial expressions, which not only can address the incompleteness of handcrafted features but also can avoid the high hardware configuration in the deep learning model. Considering some problems of overfitting and weak generalization ability of the single classifier, random forest is applied in this paper. Meanwhile, this paper makes some improvements for C4.5 classifier and the traditional random forest in the process of experiments. A large number of experiments have proved the effectiveness and feasibility of the proposed method.

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Classification of complete blood count and haemoglobin typing data by a C4.5 decision tree, a naïve Bayes classifier and a multilayer perceptron for thalassaemia screening

Cited by 37 publications

References 25 publications

Comparison of various classification algorithms in the diagnosis of type 2 diabetes in Iran

Comparison of various classification algorithms in the diagnosis of type 2 diabetes in Iran

Computer Aided System for Detection and Classification of Breast Cancer

Facial Expression Recognition Based on Random Forest and Convolutional Neural Network

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