A. Hency Juliet ¹, R. Padmajavalli ¹

Affiliations
1 Research and Development Centre, Bharathiar University, Coimbatore - 641046, Tamil Nadu, IN

Abstract

Objective: To expose the Performance of classification algorithms on endometrial cancer data. The best algorithms are listed based on the result of various test options and ranked based on their accuracies. Methods and Analysis: Classification is one of the data mining techniques used to find a model that describes the data classes or concepts. The class-label of strange instance is predicted with the help of classification. It compares the classification algorithms by measuring accuracies, speed and strength of algorithms using WEKA tool. Accuracies of classification algorithms are calculated by means of four different options. The error rate and time taken to build the model also measured. Findings: The accuracies of sixteen algorithms are measured by training set, test set, tenfold cross validation and percentage split testing options. The average accuracies are calculated, then compared and ranked with highest accuracy first. The best five algorithms are taken for final performance on endometrial cancer dataset. The accuracy of Random Forest algorithm is high, but it took 0.16 sec to build the model, whereas the IBK, Random Tree and KStar algorithms’ performs well with 0sec to build the model. Bagging algorithm takes more time to build the model. In terms of time and accuracy IBK produces better results as compared to other algorithms. Random Forest algorithm is most excellent in provisos of correctly classified occurrence. Novelty/Improvement: With the 315 instances of endometrial cancer data, the time taken to build the model is zero for IBK, KStar and Random Tree algorithms. If the number of instance increases then time also will increase.

Keywords

Classification Algorithms, Endometrial Cancer, IBK, KStar, Random Tree.

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R. Priya ¹, R. Padmajavalli ²

Affiliations
1 Research and Development Centre, Bharathiar University, Coimbatore - 641046, Tamil Nadu, IN
2 Department of Computer Applications, Bhaktavatsalam Memorial College for Women, Chennai - 600080, Tamil Nadu, IN

Abstract

Diagnosis of diseases is a difficult work that has to do in accurate manner. Text mining deals a great job in this field. A huge mass of data is available in biomedical field, using this data we can diagnosis many diseases by text mining techniques in efficient manner. Text mining methods are used to retrieve useful knowledge from large data. Objective: The aim of this paper is to review several text mining methods used in biomedical field. This survey is helpful to select a best text mining method for biomedical data. Methods/Analysis: In this paper, classification method is used to study the biomedical text mining for diagnosing diseases. In the field of biomedical, classification can be done on the basis of patient disease pattern to separate the patients into high risk or low risk The classification techniques have two methods they are Binary contains two classes and multilevel contains more than two classes. Classification method is widely used in biomedical text mining. In this paper different classification techniques can be applied to categorize the text they are SVM (Support Vector Machine) NN (Neural Network), K-NN (K-Nearest Neighbor), Bayesian Method and DT (Decision Tree). Findings: In this paper, different classification techniques were surveyed and their merits and limitations have been discussed. The various classification techniques were applied in medical data where useful patterns and knowledge were extracted. The important task is that to select the suitable data and classification method for disease diagnosis. The objective of this survey is that how the classification methods are applied in biomedical application and to select which method is suitable and efficient for diagnosis of a particular disease. Novelty/Improvement: The main advantage of the survey is that it can be applied to any kind of dataset, it is a description dataset or not. For future improvement, we will implement our proposed methodology on using some major chest diseases datasets and measured performance in terms of training time and accurate diagnosis.

Keywords

Biomedical Text Mining, Classification, Concept Linkage, IE (Information Extraction), Topic Tracking.

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