Medical Factors-Based Mobile Application for Choosing Contraceptives: A Data Mining Approach
Bala Pwa’anda Bulus,
Yusuf Musa Malgwi
Issue:
Volume 8, Issue 1, June 2023
Pages:
1-10
Received:
1 April 2023
Accepted:
28 April 2023
Published:
24 July 2023
Abstract: Women's negative experience with contraception and understanding of the experience differentials, coupled with Limited information accessibility on contraceptives to healthcare centers, is responsible for the unwillingness and discontinuation in the use of contraceptives in Nigeria. The study aims at developing a Medical Factor based Mobile application Model for contraceptive implants using K-Nearest Neighbour (KNN) and Support Vector Machine (SVM) techniques for the prediction of discomfort, and blood type. KNN and SVM techniques in R were the two methods used to develop a model to classify blood group types based on discomfort and vice versa. 10-fold cross-validation was carried out and was repeated 3 times and the optimal values were selected. The model was tested by the use of Predict function in which test data was used as the new data (input data) of the model. Experimental results showed the prediction accuracy of the KNN model was 85.72% and the SVM model was 92.2%. SVM outperformed KNN. However, the performances of models imply that the application can be used by women as the means for accessing information on discomforts associated with contraceptive implants as well as blood type. Most women with similar blood types have similar experiences (discomforts). Therefore this model can be used to choose the right contraceptive that is friendly to one blood type. The prediction mobile application of the tested model frontend was implemented in Android built-in with Java as the programming language. The backend was designed using structural query language in the WAMP server.
Abstract: Women's negative experience with contraception and understanding of the experience differentials, coupled with Limited information accessibility on contraceptives to healthcare centers, is responsible for the unwillingness and discontinuation in the use of contraceptives in Nigeria. The study aims at developing a Medical Factor based Mobile applica...
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Review Article
Machine Learning for Text Classification on Twitter: A Literature Review
Muneer Alsurori,
Ahlam Enan*,
Rahuf Alwan,
Wafa Algumaei,
Somia Alturki,
Entsar Alkahtany
Issue:
Volume 8, Issue 1, June 2023
Pages:
11-17
Received:
17 September 2023
Accepted:
16 October 2023
Published:
9 November 2023
Abstract: This literature review examines the application of machine learning (ML) techniques for text classification on Twitter. With the immense volume of data generated on social media platforms like Twitter, there is a need for automated methods to extract valuable information. ML, known for its ability to learn patterns and relationships in large datasets, has gained significant attention in this context. The purpose of this review is to explore the background and aim of ML for text classification on Twitter, the methods employed, the results obtained, and the conclusions drawn. The review begins by discussing the background and aim, emphasizing the vast amount of data available on Twitter and the need for automated techniques to extract useful information from this data. It highlights the significance of ML in addressing this challenge, particularly in tasks such as sentiment analysis, topic modeling, and spam detection, which play a crucial role in social media analysis. Next, the review provides an overview of the methods used in various studies on text classification using Twitter data. It explores the latest approaches and techniques employed in ML, including feature extraction methods like bag-of-words, n-grams, and word embeddings. It also discusses the preprocessing steps involved in preparing Twitter data for classification tasks. subsequently, the review presents the results obtained from different studies in the field. It discusses the performance metrics used to evaluate the effectiveness of ML models, highlighting measures such as accuracy, precision, recall, and F1-score. The review also discusses variations in performance across different classification tasks, providing insights into the strengths and limitations of the approaches used.
Abstract: This literature review examines the application of machine learning (ML) techniques for text classification on Twitter. With the immense volume of data generated on social media platforms like Twitter, there is a need for automated methods to extract valuable information. ML, known for its ability to learn patterns and relationships in large datase...
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