Data Mining Algorithm and the Effectiveness of Mathematics Classroom Teaching based on Support Vector Machine

SVM (Support Vector Machine) is a powerful data mining algorithm, and is mainly used to finish classification or regression tasks. In this literature, SVM is used to conduct disease prediction. We focus on integrating with stratified sample and grid search technology to improve the classification accuracy of SVM, thus, we propose an improved algorithm named SGSVM: Stratified sample and Grid search based SVM. To testify the performance of SGSVM, heart-disease data from UCI are used in our experiment, and the results show SGSVM has obvious improvement in classification accuracy, and this is very valuable especially in disease prediction.

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Efficient Data-Mining Algorithm for Predicting Heart Disease Based on an Angiographic Test

Malaysian Journal of Medical Sciences ◽

10.21315/mjms2021.28.5.12 ◽

2021 ◽

Vol 28 (5) ◽

pp. 118-129

Author(s):

Alabi Waheed Banjoko ◽

◽

Kawthar Opeyemi Abdulazeez ◽

Keyword(s):

Data Mining ◽

Support Vector Machine ◽

Heart Disease ◽

Cross Validation ◽

Classification Method ◽

Support Vector ◽

Data Mining Algorithm ◽

Machine Method ◽

Mining Algorithm ◽

Splitting Ratio

Background: The computerised classification and prediction of heart disease can be useful for medical personnel for the purpose of fast diagnosis with accurate results. This study presents an efficient classification method for predicting heart disease using a data-mining algorithm. Methods: The algorithm utilises the weighted support vector machine method for efficient classification of heart disease based on a binary response that indicates the presence or absence of heart disease as the result of an angiographic test. The optimal values of the support vector machine and the Radial Basis Function kernel parameters for the heart disease classification were determined via a 10-fold cross-validation method. The heart disease data was partitioned into training and testing sets using different percentages of the splitting ratio. Each of the training sets was used in training the classification method while the predictive power of the method was evaluated on each of the test sets using the Monte-Carlo cross-validation resampling technique. The effect of different percentages of the splitting ratio on the method was also observed. Results: The misclassification error rate was used to compare the performance of the method with three selected machine learning methods and was observed that the proposed method performs best over others in all cases considered. Conclusion: Finally, the results illustrate that the classification algorithm presented can effectively predict the heart disease status of an individual based on the results of an angiographic test.

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Construction of Multimedia Assisted Legal Classroom Teaching Model Based on Data Mining Algorithm

Scientific Programming ◽

10.1155/2021/9948800 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Yu Lu ◽

Wang Lizhi

Keyword(s):

Data Mining ◽

Phase Space ◽

Educational Resources ◽

Classroom Teaching ◽

Retrieval Algorithm ◽

Teaching Model ◽

Data Mining Algorithm ◽

Teaching System ◽

Model Based ◽

Mining Algorithm

In order to quickly and accurately retrieve a required part from massive multimedia educational resources and improve the utilization of educational resources, a multimedia assisted legal classroom teaching model based on data mining algorithm is designed. Firstly, the attributes of multimedia assisted legal classroom teaching resources are judged, and the numerical resources are standardized and discretized. Then, the B+ tree is used to establish the model’s indexes and index library, and the corresponding retrieval algorithm is designed to complete the resource search, establish the data distribution structure model of the multimedia assisted legal classroom teaching system, mine the data, reconstruct the phase space of the fused data information flow, extract the high-order moment features of the specific data in the multimedia assisted legal classroom teaching system in the reconstructed high-dimensional phase space, and realize the accurate mining of the feature data. The experimental results show that the teaching effect of the designed model has more advantages and can promote the improvement of students’ performance.

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An Efficient System for Early Diagnosis of Breast Cancer using Support Vector Machine

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.a1626.109119 ◽

2019 ◽

Vol 9 (1) ◽

pp. 7029-7035

Keyword(s):

Breast Cancer ◽

Data Mining ◽

Support Vector Machine ◽

Low Income ◽

Low Income Countries ◽

Support Vector ◽

Test Accuracy ◽

Ultrasound Images ◽

Data Mining Algorithm ◽

Efficient System

There are many lives lost every year due to cancer and among them; among the women breast cancer causes the most deaths. For the better prediction of breast cancer risks, numerous studies have been undertaken incorporating data mining techniques. 1.1 million Cases of breast cancer were reported in 2004. It has been seen over the years that, that the numbers increase with the increasing industrialization and urbanization. It was earlier observed that mostly affected countries with breast cancer were high income countries such as America but now a days it is also very serious issue in middle and low income countries like Africa, Latin America and Asia. The main objective of this paper is to create a model which can more efficiently and accurately categorize a cancer as malignant or benevolent based on interpretation of the numerical values of attributes of ultrasound images of breast cancer. In this paper various data mining algorithm used like SVM(Support Vector Machine) for prediction and compared it with various other algorithms such as CART, Logistic Regression, KNN for the best training and test accuracy. SVM algorithm gives the most accurate results among the rest algorithm.

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Evaluation of the College English Flipped Classroom Teaching Model Based on Data Mining Algorithms

Mobile Information Systems ◽

10.1155/2021/1407407 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Jiangshui Xiang

Keyword(s):

Data Mining ◽

Statistical Analysis ◽

Resource Sharing ◽

Flipped Classroom ◽

Classroom Teaching ◽

Teaching Model ◽

Data Mining Algorithm ◽

Process Data ◽

College English ◽

Mining Algorithm

Aiming at the problem of the inability to classify data due to the excessive amount of teaching resources, which leads to the college English flipped classroom teaching model’s low resource sharing rate and the poor accuracy of score statistical analysis, a university-based data mining algorithm is designed. Research on the evaluation of english flipped classroom teaching model is conducted, the strategy of applying the flipped classroom in college English teaching is analyzed, the characteristics and advantages of this model are explored, the data mining algorithm to practical teaching is applied, and the decision tree C4.5 classification technology is used to achieve accurate classification of massive student test scores. The classification technology selects classification attributes based on the information gain rate. It uses the postpruning method to process data to improve the accuracy of data classification. Finally, the statistical analysis results of the business logic layer are transmitted to the user through the browser application layer using the WEB server. The experimental results show that using this article’s evaluation method, the college English flipped classroom teaching model can achieve a high resource sharing rate, high accuracy of performance statistics analysis, and a good teaching effect.

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MODEL KLASIFIKASI KEPUASAN MAHASISWA TEKNIK TERHADAP SARANA PEMBELAJARAN MENGGUNAKAN DATA MINING

Jurnal Teknologi Informasi Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika ◽

10.47111/jti.v14i2.1222 ◽

2020 ◽

Vol 14 (2) ◽

pp. 112-118

Author(s):

Ariesta Lestari ◽

Elga Mariati ◽

Widiatry Widiatry

Keyword(s):

Data Mining ◽

Support Vector Machine ◽

Decision Tree ◽

Naive Bayes ◽

Naïve Bayes ◽

Support Vector ◽

Data Mining Algorithm ◽

Prediction System ◽

Data Mining Approach

Student in one of the stakeholder in a university. Therefore, student’s perception in the quality of learning facilities and infrastructures become important to ensure the university’s performance. The Faculty of Engineering of University of Palangka Raya has not comprehensively evaluated the students’ satisfactory of the learning’s facilities. In this research, methods from data mining approach was implemented to classify whether the students satisfy or not with the quality of the learning’s facility in Engineering Faculty. This research compared three data mining algorithm, Decision Tree C4.5, Support Vector Machine, and Naïve Bayes to obtain the best algorithm for the prediction system. 948 responses were collected, 61% of the respondent were satisfied with the quality of the learning facilities and infrastructures, while 39% of the respondents were dissatisfied. The Decision Tree c4.5 had the best performance with accuracy of 88% and precision of 98% compared to the Naïve Bayes and support vector machine.

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KLASIFIKASI SMS SPAM MENGGUNAKAN SUPPORT VECTOR MACHINE

Jurnal Pilar Nusa Mandiri ◽

10.33480/pilar.v15i2.693 ◽

2019 ◽

Vol 15 (2) ◽

pp. 275-280

Author(s):

Agus Setiyono ◽

Hilman F Pardede

Keyword(s):

Data Mining ◽

Support Vector Machine ◽

Decision Tree ◽

Naive Bayes ◽

Naïve Bayes ◽

Support Vector ◽

Spam Detection ◽

Support Vector Machine Algorithm ◽

Data Mining Techniques ◽

To Receive

It is now common for a cellphone to receive spam messages. Great number of received messages making it difficult for human to classify those messages to Spam or no Spam. One way to overcome this problem is to use Data Mining for automatic classifications. In this paper, we investigate various data mining techniques, named Support Vector Machine, Multinomial Naïve Bayes and Decision Tree for automatic spam detection. Our experimental results show that Support Vector Machine algorithm is the best algorithm over three evaluated algorithms. Support Vector Machine achieves 98.33%, while Multinomial Naïve Bayes achieves 98.13% and Decision Tree is at 97.10 % accuracy.

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