Waikato Environment For Knowledge Analysis Research Articles

The Application of Data Mining by using K Means Clustering Method in Determining New Students’ Admission Promotion Strategy

This study aims to determine the promotion strategy on the admission of new students at the university. Universities need appropriate promotion strategies to increase the number of new students enrolled in subsequent years and to fulfill the equal distribution of new students in each region and study programs at the University. Classification of new student data reception at the Indo Global Mandiri University (IGM University) in 2018/2019 uses the CRISP-DM data mining application (the Cross-Industry Standard Process for Data Mining) using the K-Means grouping method. Research data using primary and secondary data. The population and sample of the study were 1011 students using 4 (four) attributes in this study, namely the name of the student, the area of origin, the study program, and the promotion strategy (direct visit, word of mouth, media, brochures, and coming directly). This test is carried out with the Waikato Environment for Knowledge Analysis (WEKA) 3.8 tool. The results of this study indicate that the direct visit strategy is the most appropriate in the admission of new students at IGM University, amounting to 492 students with 26%, with this strategy being able to absorb many new student candidates from various regions including Palembang, Regency / City, and regions in outside South Sumatra, there is also equality in various study programs at IGM University. Word of mouth promotion strategies and media are optimized to be included in the promotion team in determining the promotion strategies in the following year to increase the number of new student admissions.

Spherical Classification of Data, a New Rule-Based Learning Method

This paper presents a new rule-based classification method that partitions data under analysis into spherical patterns. The forte of the method is twofold. One, it exploits the efficiency of distance metric-based clustering to fast collect similar data into spherical patterns. The other, spherical patterns are each a trait shared among one type of data only, hence are built for classification of new data. Numerical studies with public machine learning datasets from Lichman (2013), in comparison with well-established classification methods from Boros et al. (IEEE Transactions on Knowledge and Data Engineering, 12, 292–306, 2000) and Waikato Environment for Knowledge Analysis ( http://www.cs.waikato.ac.nz/ml/weka/ ), demonstrate the aforementioned utilities of the new method well.

A Comparative Analysis of Data Mining Techniques on Breast Cancer Diagnosis Data using WEKA Toolbox

Breast cancer is considered the second most common cancer in women compared to all other cancers. It is fatal in less than half of all cases and is the main cause of mortality in women. It accounts for 16% of all cancer mortalities worldwide. Early diagnosis of breast cancer increases the chance of recovery. Data mining techniques can be utilized in the early diagnosis of breast cancer. In this paper, an academic experimental breast cancer dataset is used to perform a data mining practical experiment using the Waikato Environment for Knowledge Analysis (WEKA) tool. The WEKA Java application represents a rich resource for conducting performance metrics during the execution of experiments. Pre-processing and feature extraction are used to optimize the data. The classification process used in this study was summarized through thirteen experiments. Additionally, 10 experiments using various different classification algorithms were conducted. The introduced algorithms were: Naive Bayes, Logistic Regression, Lazy IBK (Instance-Bases learning with parameter K), Lazy Kstar, Lazy Locally Weighted Learner, Rules ZeroR, Decision Stump, Decision Trees J48, Random Forest and Random Trees. The process of producing a predictive model was automated with the use of classification accuracy. Further, several experiments on classification of Wisconsin Diagnostic Breast Cancer and Wisconsin Breast Cancer, were conducted to compare the success rates of the different methods. Results conclude that Lazy IBK classifier k-NN can achieve 98% accuracy among other classifiers. The main advantages of the study were the compactness of using 13 different data mining models and 10 different performance measurements, and plotting figures of classifications errors.

A Model for Prediction of Kidney Cancer Using Data Analytics Technique

Across the globe, kidney cancer and other cancerous diseases has been a threat to human lives. The incidence and mortality rate represent a significant and growing threat to both developed and developing countries especially in Africa, where most cancers are diagnosed at an advanced stage. This typically contributes to its complications and high rate of mortality, and has been attributed to limited awareness of early signs and symptoms of the disease, lack of detective mechanism and inaccessible cancer care in our health care centres. To preclude the harm and mortality caused by the disease, an intelligent mechanism for early prediction and prognosis of the syndrome is vital. However, early detection and prognosis requires an accurate information and analytic procedure that will assist and equip the health-care providers/public with the skills to identify early the indicators of the disease. Efforts in this work, produced a model for early prediction of kidney cancer using data analytic approach. Dataset and reports pertaining to the disease were acquired from selected private and public hospitals in fifty-two (52) selected LGA in Nigeria. A two-layered classifier system consisting of Artificial Neural Networks (ANN) and Decision Tree (DT) designed for the work was successfully employed in the model building. Waikato Environment for Knowledge Analysis (WEKA) platform was used for the experiment. The performance of the classifiers considered was compared using standard metrics of accuracy and time taken as benchmark. Experimental results show that the J48 decision tree algorithm outperform all other algorithms in the classifier family with correctly classified instances of 74.7%, F-Measure of 0.614, TP rate of 0.747, FP rate of 0.135, precision and recall of 0.687 and 0.714 respectively. It took the algorithm, 0.03 seconds to build the model. The performance of this algorithm proved its suitability as a valuable tool for the research purpose. The model will in no small measure support the efforts of the national health scheme in preventing the disease mortality rate.

Diagnosis of Headaches Types Using Artificial Neural Networks and Bayesian Networks

The aim of this paper is to classify several types of headaches from patients, using different data analysis methods, with the application of two classifying algorithms, comparatively: Bayesian Networks (BN) and Artificial Neural Networks (ANN). The data was collected through a data survey process applied to 2,177 patients with headache diagnosis in Neurological Clinic of municipality of Joinville/SC, Brazil, from January 2010 to November 2014. It was constructed eight different Test Models, varying: the attributes codification (codification A and B); the network output (one and five) and the classifier algorithms (BN and ANN). It is presented the accuracy results obtained from the eight Test Models, using the software WEKA® (Waikato Environment for Knowledge Analysis). The results presented a good accuracy in all tests realized and they suggest that BNs give better accuracy when comparing to ANNs for this problem.

Comparative Analytics of Classifiers on Resampled Datasets for Pregnancy Outcome Prediction

The main challenges of predictive analytics revolve around the handling of datasets, especially the disproportionate distribution of instances among classes in addition to classifier-suitability issues. This unequal spread causes imbalance learning and severely obstructs prediction accuracy. In this paper, the performances of six classifiers and the effect of data balancing (DB) and formation approaches for predicting pregnancy outcome (PO) were investigated. Synthetic minority oversampling technique (SMOTE), resampling with and without replacement, were adopted for data imbalance treatment. Six classifiers including random forest (RF) were evaluated on each resampled dataset with four test modes using Waikato Environment for Knowledge Analysis and R programming libraries. The results of analysis of variance performed separately using F-measure and root mean squared error showed that mean performance of classifiers across the datasets varied significantly (F=117.9; p=0.00) at 95% confidence interval, while turkey multi-comparison test revealed RF(mean=0.78) and SMOTE (mean=0.73) as having significantly different means. The RF model on SMOTE produced each PO class accuracy ≥0.89, area under the curve ≥ 0.96 and coverage of 97.8% and was adjudged the best classifier-DB method pair. However, there was no significant difference (F=0.07, 0.01; p=1.000) in the mean performances of classifiers across test data modes respectively. It reveals that train/test data modes insignificantly affect classification accuracy, although there are noticeable variations in computational cost. The methodology significantly enhance the predictive accuracy of minority classes and confirms the importance of data-imbalance treatment, and the suitability of RF for PO classification.

CYBER SECURITY FRAMEWORK FOR NIGERIAN CIVIL AVIATION AUTHORITY, HEADQUARTERS

The practice of defending assets from unauthorized access, use, disclosure, disruption, modification, perusal, inspection, recording or destruction is as ancient as man’s existence on earth. Cybersecurity, information security, and computer security arefurther attempts of man to continue the same ancient practice. This is due to the unavoidably prevalent need and use of information and communication technologies in this century, which has left the man with such vulnerability and insecuritythrough its devices and infrastructures. The aviation industry, being one of the most economic and leisure viable industry in theworld has been at the top-notch of the use of these technologies for better services to man. Nigeria Aviation industry is one of thelargest and most viable in Africa is opened to the vulnerabilities and cyber threats that comes with these advanced technologies.However, several methods and programs have been employed by various organizations and stakeholders, but, the Nigerian CivilAviation Authority (NCAA), been the apex governing body that control, monitor and regulate the economic and safety oversight ofthe entire Aviation industry in Nigeria seems not to have gotten it right with the recent trends of cyber warfare in the industry asopposed to their counterparts in the world over. Therefore, this research study attempts to employ data from existing literatureand questionnaires to analyze the existing practices of cyber/information security programs (if any) within the NCAA and todesign an adaptable cybersecurity framework that is robust enough for its safety. By using version 3.9.3 of Waikato Environmentfor Knowledge Analysis (WEKA) software, Naive Bayes, Bayes Net, Decision Table, IBK (Linear NNSearch), REP Tree, J48, SMO, Simple Logistic, and Bagging are the nine algorithms employed to determine and predict tier classes as existing profile and target profile respectively for NCAA within the framework. SMO emerge best fit to predict the target profile while the Bagging algorithm emerges best fit to determine the current profile. Gaps and implementable action plans were deduced. Thereafter, the RNA Cyber Security Framework (RNA-CSF) is conceptually developed, analyzed and proposed for NCAA and for any other organization that has the need to use it.

Usage Analysis of Smartphone with Hierarchical Clustering

Functional and Non Functional attributes are the important factors of the Smartphone. This paper deals with how to group the characteristics of mobile phone using the clustering algorithms and then obtained the results after classify the related algorithms. Classify all the functional and non-functional attributes of the smartphone using the latest data mining algorithms using WEKA (Waikato Environment for Knowledge Analysis) open-source software to analyze data.

Implementation of the K-Means Clustering Method in Data Grouping Sales In Asia Africa Dentures Dental

Data mining can be applied to explore the added value of a set of data in the form of knowledge that had been unknown to them manually. There are several techniques used dala mining eyes, one satuteknik data mining is clustering. Clustering can be used for grouping to something. As can group sales data that is most desirable, and others. Examples of companies engaged in the sale is a dental african Asia. Asia Africa Dental is one area of business engaged in the sale of false teeth. Asia Africa Dental these every day to meet the needs of consumers. But Asia Africa Dental lacking in reviewing products sold. What products are needed consumer and data storage is less effective. Thus the need for a system that can support the company in taking decisions quickly and precisely. So in this study, the authors used the application of K-Means Clustering method. To facilitate the author in analyzing the K-Means Clustering The author using the application Weka (Waikato Environment for Knowledge Analysis) .. The result of the calculation Weka (Waikato Environment for Knowledge Analysis) is inserted into the Visual Basic .Net.

Predicción de accidentes viales en Cartagena, Colombia, con árboles de decisión y reglas de asociación

El objetivo principal de esta investigación es predecir los factores asociados con la severidad en los accidentes viales de Cartagena (Colombia), la metodología está basada en técnicas de minería de datos como arboles de decisión (J48) y reglas de asociación (soporte, confianza, Lift). La investigación fue desarrollada con 10.053 registros de accidentes de tráfico entre 2016 y 2017, por medio del uso del Software WEKA (Waikato Environment for Knowledge Analysis). En el análisis, la severidad fue definida de bajo riesgo (daños materiales), y alto riesgo (víctimas heridas y fatales), y su validación consideró la técnica transversal 10fold. Entre los resultados más significativos, se evidenció que los motociclistas y ciclistas son los actores viales más vulnerables, además los moto–usuarios entre los 20–39 años son propensos a accidentes viales con alta severidad. Finalmente, los factores de accidentalidad vial identificados ayudan a promover contramedidas para mejorar la seguridad vial de la ciudad.

Predicting skilled delivery service use in Ethiopia: dual application of logistic regression and machine learning algorithms

BackgroundSkilled assistance during childbirth is essential to reduce maternal deaths. However, in Ethiopia, which is among the six countries contributing to more than half of the global maternal deaths, the coverage of births attended by skilled health personnel remains very low. The aim of this study was to identify determinants and develop a predictive model for skilled delivery service use in Ethiopia by applying logistic regression and machine-learning techniques.MethodsData from the 2016 Ethiopian Demographic and Health Survey (EDHS) was used for this study. Statistical Package for Social Sciences (SPSS) and Waikato Environment for Knowledge Analysis (WEKA) tools were used for logistic regression and model building respectively. Classification algorithms namely J48, Naïve Bayes, Support Vector Machine (SVM), and Artificial Neural Network (ANN) were used for model development. The validation of the predictive models was assessed using accuracy, sensitivity, specificity, and area under Receiver Operating Characteristics (ROC) curve.ResultsOnly 27.7% women received skilled delivery assistance in Ethiopia. First antenatal care (ANC) [AOR = 1.83, 95% CI (1.24–2.69)], birth order [AOR = 0.22, 95% CI (0.11–0.46)], television ownership [AOR = 6.83, 95% CI (2.52–18.52)], contraceptive use [AOR = 1.92, 95% CI (1.26–2.97)], cost needed for healthcare [AOR = 2.17, 95% CI (1.47–3.21)], age at first birth [AOR = 1.96, 95% CI (1.31–2.94)], and age at first sex [AOR = 2.72, 95% CI (1.55–4.76)] were determinants for utilizing skilled delivery services during the childbirth. Predictive models were developed and the J48 model had superior predictive accuracy (98%), sensitivity (96%), specificity (99%) and, the area under ROC (98%).ConclusionsFirst ANC and contraceptive uses were among the determinants of utilization of skilled delivery services. A predictive model was developed to forecast the likelihood of a pregnant woman seeking skilled delivery assistance; therefore, the predictive model can help to decide targeted interventions for a pregnant woman to ensure skilled assistance at childbirth. The model developed through the J48 algorithm has better predictive accuracy. Web-based application can be build based on results of this study.

Machine Learning Approaches to Develop Pedotransfer Functions for Tropical Sri Lankan Soils

Poor data availability on soil hydraulic properties in tropical regions hampers many studies, including crop and environmental modeling. The high cost and effort of measurement and the increasing demand for such data have driven researchers to search for alternative approaches. Pedotransfer functions (PTFs) are predictive functions used to estimate soil properties by easily measurable soil parameters. PTFs are popular in temperate regions, but few attempts have been made to develop PTFs in tropical regions. Regression approaches are widely used to develop PTFs worldwide, and recently a few attempts were made using machine learning methods. PTFs for tropical Sri Lankan soils have already been developed using classical multiple linear regression approaches. However, no attempts were made to use machine learning approaches. This study aimed to determine the applicability of machine learning algorithms in developing PTFs for tropical Sri Lankan soils. We tested three machine learning algorithms (artificial neural networks (ANN), k-nearest neighbor (KNN), and random forest (RF)) with three different input combination (sand, silt, and clay (SSC) percentages; SSC and bulk density (BD); SSC, BD, and organic carbon (OC)) to estimate volumetric water content (VWC) at −10 kPa, −33 kPa (representing field capacity (FC); however, most studies in Sri Lanka use −33 kPa as the FC) and −1500 kPa (representing the permanent wilting point (PWP)) of Sri Lankan soils. This analysis used the open-source data mining software in the Waikato Environment for Knowledge Analysis. Using a wrapper approach and best-first search method, we selected the most appropriate inputs to develop PTFs using different machine learning algorithms and input levels. We developed PTFs to estimate FC and PWP and compared them with the previously reported PTFs for tropical Sri Lankan soils. We found that RF was the best algorithm to develop PTFs for tropical Sri Lankan soils. We tried to further the development of PTFs by adding volumetric water content at −10 kPa as an input variable because it is quite an easily measurable parameter compared to the other targeted VWCs. With the addition of VWC at −10 kPa, all machine learning algorithms boosted the performance. However, RF was the best. We studied the functionality of finetuned PTFs and found that they can estimate the available water content of Sri Lankan soils as well as measurements-based calculations. We identified RF as a robust alternative to linear regression methods in developing PTFs to estimate field capacity and the permanent wilting point of tropical Sri Lankan soils. With those findings, we recommended that PTFs be developed using the RF algorithm in the related software to make up for the data gaps present in tropical regions.

CLASSIFICATION OF SOFTWARE CONTROL ARCHITECTURES FOR A POWERED PROSTHESIS THROUGH CONVENTIONAL GAIT ANALYSIS USING MACHINE LEARNING APPLICATIONS

The powered prosthesis for people with transtibial amputation offers the opportunity to more appropriately restore gait functionality with benefits, such as powered plantar flexion. In particular, various software control architectures provide unique capabilities for regulating the powered prosthesis during gait. One highly novel approach applies the winding filament hypothesis, which enables an advanced modeling of muscle characteristics, such as through introducing the attributes of titin into the muscle model. The objective of the research is to contrast the conventional control architecture of the BiOM-powered prosthesis compared with the winding filament hypothesis control architecture through machine learning classification. Four machine learning algorithms are applied through the Waikato Environment for Knowledge Analysis (WEKA): J48 decision tree, [Formula: see text]-nearest neighbors, logistic regression, and the support vector machine. The feature set is derived from the force signal acquired from a force plate, which is a conventional gait analysis system. The feature set applied five attributes representing temporal and kinetic aspects of the stance phase of gait. The [Formula: see text]-nearest neighbors algorithm achieves the best machine learning classification accuracy of 95%. The preliminary research establishes the foundation for more sophisticated endeavors respective of the powered prosthesis, such as determining the appropriateness of modifying the software control architecture to best accommodate the progressive lifestyle evolutions and adaptations of the person with amputation.

Student Learning Prediction Using Machine Learning Techniques

Now a day’s e-learning is smartly growing technology. This technology is more helpful for students to communicate with their professors through chats or emails. E-Learning also removes the obstacle of physical presence of an E-learner. The main aim of this paper is to predict student performance in their final exams using different machine learning techniques. Information like attendance, marks, assignments, class participation, seminar, CA, projects and semester are collected to predict student performance. This prediction helps the instructors to analyze their students based on their performance. For that we have used WEKA tool for the prediction of the student performance. WEKA (Waikato Environment for Knowledge Analysis) is one of the data mining too which is used for the classification and clustering using data mining algorithms. This prediction helps the students and the staffs to know how much effort their students need to be put in their final exams to get good marks.

Automatic Machine-Learning-Based Outcome Prediction in Patients With Primary Intracerebral Hemorrhage.

Background: A predictive model can provide physicians, relatives, and patients the accurate information regarding the severity of disease and its predicted outcome. In this study, we used an automated machine-learning-based approach to construct a prognostic model to predict the functional outcome in patients with primary intracerebral hemorrhage (ICH).Methods: We retrospectively collected data on demographic characteristics, laboratory studies and imaging findings of 333 patients with primary ICH. The functional outcomes at the 1st and 6th months after ICH were defined by the modified Rankin scale. All of the attributes were used for preprocessing and for automatic model selection with Automatic Waikato Environment for Knowledge Analysis. Confusion matrix and areas under the receiver operating characteristic curves (AUC) were used to test the predictive performance.Results: Among the models tested, the random forest provided the best predictive performance for functional outcome. The overall accuracy for predicting the 1st month outcome was 83.1%, with 77.4% sensitivity and 86.9% specificity, and the AUC was 0.899. The overall accuracy for predicting the 6th month outcome was 83.9%, with 72.5% sensitivity and 90.6% specificity, and the AUC was 0.917.Conclusions: Using an automatic machine learning technique to predict functional outcome after ICH is feasible, and the random forest model provides the best predictive performance across all tested models. This prediction model may provide information regarding functional outcome for clinicians that will help provide appropriate medical care for patients and information for their caregivers.

Discretization method based on binary ant colony and variable precision rough set

Discretization plays an important role in data pre-processing and has been used in fields such as artificial intelligence and data mining. A new discretization algorithm based on binary ant colony and variable precision rough set is proposed in this paper. Binary ant network is first built using candidate breakpoints, then global optimal breakpoints is searched in the network. The fitness function is established by the number of breakpoints and approximation classification accuracy of variable precision rough set. This method is compared with other algorithms using C4.5 classifier on the WEKA (Waikato Environment for Knowledge Analysis) platform and seven UCI data sets. The results indicate that the proposed method performs well.

Performance evaluation of classifiers for the recognition of offline handwritten Gurmukhi characters and numerals: a study

Classification is a process to pull out patterns from a number of classes by using various statistical properties and artificial intelligence techniques. The problem of classification is considered as one of the important problems for the development of applications and for efficient data analysis. Based on the learning adaptability and capability to solve complex computations, classifiers are always the best suited for the pattern recognition problems. This paper presents a comparative study of various classifiers and the results achieved for offline handwritten Gurmukhi characters and numerals recognition. Various classifiers used and evaluated in this study include k-nearest neighbors, linear-support vector machine (SVM), RBF-SVM, Naive Bayes, decision tree, convolution neural network and random forest classifier. For the experimental work, authors used a balanced data set of 13,000 samples that includes 7000 characters and 6000 numerals. To assess the performance of classifiers, authors have used the Waikato Environment for Knowledge Analysis which is an open source tool for machine learning. The performance is assessed by considering various parameters such as accuracy rate, size of the dataset, time taken to train the model, false acceptance rate, false rejection rate and area under receiver operating characteristic Curve. The paper also highlights the comparison of correctness of tests obtained by applying the selected classifiers. Based on the experimental results, it is clear that classifiers considered in this study have complementary rewards and they should be implemented in a hybrid manner to achieve higher accuracy rates. After executing the experimental work, their comparison and analysis, it is concluded that the Random Forest classifier is performing better than other recently used classifiers for character and numeral recognition of offline handwritten Gurmukhi characters and numerals with the recognition accuracy of 87.9% for 13,000 samples.

Disentangling factors limiting diamondback moth, Plutella xylostella (L.), spatio‐temporal population abundance: A tool for pest forecasting

AbstractData‐mining techniques play an important role in hyperparameter optimization of heterogeneous environmental factors and their relative contribution as determinants of incidences in insect pest ecological studies. A multidimensional field‐based surveillance was conducted in two seasons (24 months), July–June of each season (2015/2016 ‐ season 1 and 2016/2017 ‐ season 2) using sex‐pheromone‐baited traps and Thermocron i‐Buttons to identify key determinants of population abundance of diamondback moth, Plutella xylostella L., across spatial horticultural hotspots of Botswana. The moth is a notorious global brassica pest. Pearson's product moment correlation matrix showed month of the year (M), mean temperature (Tmean) and maximum temperature (Tmax) as positively correlated (p < 0.001) to number of moths (N), while minimum temperature (Tmin), minimum relative humidity (RHmin), mean relative humidity (RHmean), maximum relative humidity (RHmax) and host plant (h) were negatively correlated (p < 0.001) to N. Using Waikato Environment for Knowledge Analysis (WEKA) data‐mining techniques, two models were developed: (a) M5P decision‐tree algorithm associated with nine linear models (LMs) and (b) principal component analysis (PCA) based on four principal components. Both approaches identified M as the major predictor of moth abundance, followed by h and farming region (R). However, R was a function of Tmax (positive auto‐correlation) and RHmax (negative auto‐correlation). These results provide simplified relative contribution of heterogeneous factors in influencing P. xylostella spatio‐temporal abundance, essential for early warning systems in pest management. This is an important component of sustainable pest management aimed at managing insect pests and minimizing pesticides abuse in brassica production systems.

A prediction of leaf mechanical properties with data mining

The mechanical properties of the leaf are typically determined by mechanical testing approaches to study the leaf lifespan, plant anti-herbivore defences and ecological functions by considering habitat influences, environmental resources variation and species diversity. While the leaf morphology features were commonly used for plant recognition and plant disease detection with the aid of an automated inspection system. However, the influence of morphology features on leaf mechanical properties is vague. In this research, we investigated the effect of various morphological features on mechanical properties of leaf, followed by proposed a novel leaf mechanical properties prediction model using data mining techniques. A 600 × 22 feature vector was collected and examined using Pearson correlation analysis and Welch’s test to select the relevant features. The prediction on four mechanical properties indicators was performed with LinearRegression, KStar, DecisionTable and M5P algorithms in the Waikato Environment for Knowledge Analysis (WEKA). The experimental results show that numeric prediction for Tearing Force (FT) and Tearing Strength (ST) (RRSE ≈ 25%) were about two folds better as compared to Work-to-tear (WT) and Specific Work-to-tear (SWT) (RRSE ≈ 50%) in four algorithms tested. The best results achieved was the FT indicator prediction with M5P algorithms (RRSE = 23.12%). FT indicator prediction model adopted from M5P algorithms output was constructed.

Breast Cancer Decisive Parameters for Iraqi Women via Data Mining Techniques

Objective This research investigates Breast Cancer real data for Iraqi women, these data are acquired manually from several Iraqi Hospitals of early detection for Breast Cancer. Data mining techniques are used to discover the hidden knowledge, unexpected patterns, and new rules from the dataset, which implies a large number of attributes.
 Methods Data mining techniques manipulate the redundant or simply irrelevant attributes to discover interesting patterns. However, the dataset is processed via Weka (The Waikato Environment for Knowledge Analysis) platform. The OneR technique is used as a machine learning classifier to evaluate the attribute worthy according to the class value.
 Results The evaluation is performed using a training data rather than cross validation. The decision tree algorithm J48 is applied to detect and generate the pattern of attributes, which have the real effect on the class value. Furthermore, the experiments are performed with three machine learning algorithms J48 decision tree, simple logistic, and multilayer perceptron using 10-folds cross validation as a test option, and the percentage of correctly classified instances as a measure to determine the best one from them. As well as, this investigation used the iteration control to check the accuracy gained from the three mentioned above algorithms. Hence, it explores whether the error ratio is decreasing after several iterations of algorithm execution or not.
 Conclusion It is noticed that the error ratio of classified instances are decreasing after 5-10 iterations, exactly in the case of multilayer perceptron algorithm rather than simple logistic, and decision tree algorithms. This study realized that the TPS_pre is the most common effective attribute among three main classes of examined dataset. This attribute highly indicates the BC inflammation.