An Improved C4.5 Algorithm using Principle of Equivalent of Infinitesimal and Arithmetic Mean Best Selection Attribute for Large Dataset

Muhammad, L. J.; Jibrin, Muhammed Besiru; Yahaya, Basira; Jibrin, I.A. Mohammed Besiru; Ahmad, Ahmad Sukri; Amshi, Jamila Musa

doi:10.1109/iccke50421.2020.9303622

Cited by 9 publications

(4 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…K-nearest neighbor is a supervised machine learning algorithm that uses existing cases and classifies new cases based on the of similarity measure [13,32]. The algorithm classifies a new case by votes of the majority of its neighbors with the case that is being given the most common category among its closest neighbors measure by a distance function.…”

Section: K-nearest Neighbormentioning

confidence: 99%

“…Machine learning is automatically discovering useful information and identifying hidden patterns in large data warehouses [16]. ML involves few phases from raw data collection to some interesting patterns, and this process includes data cleaning, transformation, selection, evaluation and knowledge presentations to provide users with explored knowledge [13]. ML algorithms have been used in the health care industry to get the meaningful insights for better diagnosic decision making [3,5,12].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning Predictive Models for Coronary Artery Disease

et al. 2021

Self Cite

View full text Add to dashboard Cite

Coronary artery disease (CAD) is the commonest type of heart disease and over 80% of the deaths resulted from the diseases occurred in developing countries including Nigeria, with majority being in those victims are below 70 years of age. Though, CAD is not a well known disease in Nigeria but however in year 2014, 2.82% of the total of deaths occurred in the country were due to the disease. In this study, a machine leaning predictive models for CAD has been developed with diagnostic CAD dataset obtained in the two General Hospitals in Kano State—Nigeria. The dataset applied on machine learning algorithms which include support vector machine, K nearest neighbor, random tree, Naïve Bayes, gradient boosting and logistic regression algorithms to build the predictive models and the models were evaluated based accuracy, specificity, sensitivity and receiver operating curve (ROC) performance evaluation techniques. In terms of accuracy random forest-based machine learning model emerged to be the best model with 92.04%, for specificity Naive Bayes based machine learning model emerged to be the best model with 92.40%, while for sensitivity support vector machine based machine learning model emerged to be the best model with 87.34% and for ROC, random forest-based machine learning model emerged to be the best model with 92.20%. The decision tree generated with random forest machine learning algorithm which happened to be best model in terms accuracy and ROC can be converted into production rules and be used develop expert system for diagnosis of CAD patients in Nigeria.

show abstract

Section: K-nearest Neighbormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machine Learning Predictive Models for Coronary Artery Disease

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…According to LSTM-RNN deep learning based forecasting models; the COVID-19 infection cases in Nigeria would continue rising. Likewise, according the models, the COVID-19 the infection cases in South Africa and Botswana, would continue going down slowly and possibly raise again, this could be due to emergence Omicron and low vaccination rate of the population in the country [39][40][41][42][43].…”

Section: Discussionmentioning

confidence: 99%

CNN-LSTM deep learning based forecasting model for COVID-19 infection cases in Nigeria, South Africa and Botswana

et al. 2022

Self Cite

View full text Add to dashboard Cite

Background COVID-19 pandemic has indeed plunged the global community especially African countries into an alarming difficult situation culminating into a great deal amounts of catastrophes such as economic recession, political instability and loss of jobs. The pandemic spreads exponentially and causes loss of lives. Following the outbreak of the omicron new variant of concern, forecasting and identification of the COVID-19 infection cases is very vital for government at various levels. Hence, having knowledge of the spread at a particular point in time, swift actions can be taken by government at various levels with a view to accordingly formulate new policies and modalities towards minimizing the trajectory of the consequences of COVID-19 pandemic to both public health and economic sectors. Methods Here, a potent combination of Convolutional Neural Network (CNN) learning algorithm along with Long Short Term Memory (LSTM) learning algorithm has been proposed in this work in order to produce a hybrid of a deep learning algorithm Convolutional Neural Network -Long Short Term Memory (CNN-LSTM) for forecasting COVID-19 infection cases particularly in Nigeria, South Africa and Botswana. Forecasting models for COVID-19 infection cases in Nigeria, South Africa and Botswana, were developed for 10 days using deep learning-based approaches namely CNN, LSTM and CNN-LSTM deep learning algorithm respectively. Results The models were evaluated on the basis of four standard performance evaluation metrics which include accuracy, MSE, MAE and RMSE respectively. However, the CNN-LSTM deep learning-based forecasting model achieved the best accuracy of 98.30%, 97.60%, and 97.74% for Nigeria, South Africa and Botswana respectively; and in the same manner, achieved lesser MSE, MAE and RMSE values compared to models developed with CNN and LSTM respectively. Conclusions Taken together, the CNN-LSTM deep learning-based forecasting model for COVID-19 infection cases in Nigeria, South Africa and Botswana dramatically surpasses the two other DL based forecasting models (CNN and LSTM) for COVID-19 infection cases in Nigeria, South Africa and Botswana in terms of not only the best accuracy of with 98.30%, 97.60%, and 97.74% but also in terms of lesser MSE, MAE and RMSE.

show abstract

“…The method creates a decision tree that may be used to categorize new data based on the properties or features of the data after being provided a set of data representing objects that are already categorized (Maulida, R., 2020). The C4.5 algorithm's decision tree can be used to categorize both discrete and continuous data (Muhammad, L. J., Besiru Jibrin, M., Yahaya, B. Z., Mohammed Besiru Jibrin, I.…”

Section: Introductionmentioning

confidence: 99%

Classification Academic Data using Machine Learning for Decision Making Process

Haerani

Syafria

Lestari

et al. 2023

JAETS

View full text Add to dashboard Cite

One of the qualities of higher education is determined by the success rate of student learning. Assessment of student success rates is based on student graduation on time. Sultan Syarif Kasim State Islamic University Riau is one of the state universities in Riau, with a total of 30,000 students. Of all the active students, there are some who are not. Students who are not active in this case will affect the timeliness of their graduation. The university always evaluates the performance of its students to find out information related to the factors that cause students to become inactive so that they are more likely to drop out and what data affect students being able to graduate on time. The evaluation results are stored in an academic database so that the data can later be used as supporting data when making decisions by the university. This research used data science concepts to explore and extract data sets from databases to find models or patterns, as well as new insights that can be used as tools for decision-making. After the data was explored, machine learning concepts were used to identify and classify the data. The method used was the Decision Tree Method. The results of the study found that these two concepts can provide the expected results. Based on the test results, it is known that the attribute that influences the success of student studies is the grade point average (GPA), where the accuracy of the maximum recognition rate is 88.19%. Keywords : Data science; Decision Tree; Graduate on Time; Machine Learning;

show abstract

An Improved C4.5 Algorithm using Principle of Equivalent of Infinitesimal and Arithmetic Mean Best Selection Attribute for Large Dataset

Cited by 9 publications

References 18 publications

Machine Learning Predictive Models for Coronary Artery Disease

Machine Learning Predictive Models for Coronary Artery Disease

CNN-LSTM deep learning based forecasting model for COVID-19 infection cases in Nigeria, South Africa and Botswana

Classification Academic Data using Machine Learning for Decision Making Process

Contact Info

Product

Resources

About