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All Journal International Journal of Artificial Intelligence Research Knowledge Engineering and Data Science ILKOM Jurnal Ilmiah Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics Indonesian Journal of Data and Science Buletin Sistem Informasi dan Teknologi Islam Ilmu Komputer untuk Masyarakat Open Community Service Journal Bulletin of Social Informatics Theory and Application Jurnal Pengabdian Masyarakat Kesehatan Gigi FOKGII
Salim, Yulita
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Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Mathematics Public Health Social Sciences Other

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Optimal Strategy for Handling Unbalanced Medical Datasets: Performance Evaluation of K-NN Algorithm Using Sampling Techniques Salim, Yulita; Utami, Aulia Putri; Manga, Abdul Rachman; Azis, Huzain; Admojo, Fadhila Tangguh
Knowledge Engineering and Data Science
Publisher : citeus

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Abstract

This study addresses the critical role of medical image classification in enhancing healthcare effectiveness and tackling the challenges of imbalanced medical datasets. It focuses on optimizing classification performance by integrating Canny edge detection for segmentation and Hu-moment feature extraction and applying oversampling and undersampling techniques. Five diverse medical datasets were utilized, covering Alzheimer’s and Parkinson’s diseases, COVID-19, brain tumours, and lung cancer. The K-Nearest Neighbors (K-NN) algorithm was implemented to enhance classification accuracy, aiming to develop a more robust framework for medical image analysis. The evaluation, conducted using cross-validation, demonstrated notable improvements in key metrics. Specifically, oversampling significantly enhanced lung cancer detection accuracy, while undersampling contributed to balanced performance gains in the COVID-19 class. Metrics, including accuracy, precision, recall, and F1-score, provided insights into the model’s effectiveness. These findings highlight the positive impact of data balancing techniques on K-NN performance in imbalanced medical image classification. Continued research is essential to refine these techniques and improve medical diagnostics.
Co-Authors Admojo, Fadhila Tangguh Adnan, Adam Anas, Risnayanti As'ad, Ihwana Aswin, Hafiza Awangga, Narendra Aziz, Huzain Bangsawan, Muhammad Hari Dwiyanti, Sri Ulfa Ericha Apriliyani Fajar AR, Muh. Aqil Febriany, Mila Haviluddin Haviluddin Hermany, Nurul Inayah Huzain Azis Irawati Irawati Ismail, Wawan jabir, sitti rahmah lilis nurhayati Manga, Abdul Rachman Mashur, Mulyanul Ilmi Muhammad Arfah Asis Muis, Ismunandar Nanda, As'syahrin Nur bahri, Nur Bahri Nur Indah, Nur Nurul A'ayunnisa Pamewa, Kurniaty Purnawansyah Purnawansyah Puspitasari, Yustisia Raja, Roesman Ridwan Ramdan Satra Ramdaniah, Ramdaniah Rasyid, Reskyanto Syafie, Lukman Umalekhoa, Alfi Syahrin Umar, Fitriyani Utami, Aulia Putri