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All Journal Journal of Applied Data Sciences
Sheeba Armoogum
University of Mauritius, Reduit 80837, Mauritius
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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Breast Cancer Prediction Using Metrics-Based Classification Sheeba Armoogum; Deshinta Arrova Dewi; Motean Kezhilen; Dedi Trinawarman
Journal of Applied Data Sciences Vol 5, No 3: SEPTEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i3.351

Abstract

Breast cancer remains the most prevalent form of cancer among women, with rising mortality rates worldwide. Early detection and accurate classification are crucial for improving patient outcomes, but manual detection methods are often time-consuming, complex, and prone to inaccuracies. This study aims to develop a machine learning (ML)-based desktop application to automate the detection and classification of breast cancer, thereby improving the efficiency and accuracy of diagnosis. Various ML algorithms, including Random Forest, Decision Tree, Support Vector Machine, Logistic Regression, Gaussian Naive Bayes, and K-nearest Neighbors, were employed to build classification models. The Wisconsin Diagnostic Breast Cancer (WDBC) dataset was used, and pre-processing techniques such as data cleaning, over-sampling, and feature selection were applied to optimize model performance. Experimental results demonstrate that the Random Forest classifier outperformed the other models, achieving an accuracy of 95.54%, precision of 96.72%, recall (sensitivity) of 95.16%, specificity of 96%, and an F1-score of 95.93%. These results highlight the potential of ML techniques in enhancing breast cancer diagnosis by offering a more reliable and efficient classification process. Future work could focus on improving feature selection techniques and applying the model to more diverse datasets for broader applicability.
Unveiling Criminal Activity: a Social Media Mining Approach to Crime Prediction Sheeba Armoogum; Deshinta Arrova Dewi; Vinaye Armoogum; Nicolas Melanie; Tri Basuki Kurniawan
Journal of Applied Data Sciences Vol 5, No 3: SEPTEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i3.350

Abstract

Social media platforms have become breeding grounds for abusive comments, necessitating the use of machine learning to detect harmful content. This study aims to predict abusive comments within a Mauritian context, focusing specifically on comments written in Mauritian Kreol, a language with limited natural language processing tools. The objective was to build and evaluate four machine learning models—Decision Tree, Random Forest, Naïve Bayes, and Support Vector Machine (SVM)—to accurately classify comments as abusive or non-abusive. The models were trained and tested using k-fold cross-validation, and the Decision Tree model outperformed others with 100% precision and recall, while Random Forest followed with 99% accuracy. Naïve Bayes and SVM, although achieving 100% precision, had lower recall rates of 35% and 16%, respectively, due to imbalanced data in the training set. Pre-processing steps, including stop-word removal and a custom Kreol spell checker, were key in enhancing model performance. The study provides a novel contribution by applying machine learning in a Mauritian context, demonstrating the potential of AI in detecting abusive language in underrepresented languages. Despite limitations such as the absence of a Kreol lemmatization tool and incomplete coverage of Kreol spelling variations, the models show promise for wider application in social media crime detection. Future research could explore expanding this approach to other languages and domains of social media crimes.
Co-Authors Dedi Trinawarman Deshinta Arrova Dewi Deshinta Arrova Dewi Motean Kezhilen Nicolas Melanie Tri Basuki Kurniawan Vinaye Armoogum