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Yulita Salim
Program Studi Teknik Informatika Fakultas Ilmu Komputer Univeristas Muslim Indonesia
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Transportation

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Optimizing classification models for medical image diagnosis: a comparative analysis on multi-class datasets Abdul Rachman Manga; Aulia Putri Utami; Huzain Azis; Yulita Salim; Amaliah Faradibah
Computer Science and Information Technologies Vol 5, No 3: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v5i3.p205-214

Abstract

The surge in machine learning (ML) and artificial intelligence has revolutionized medical diagnosis, utilizing data from chest ct-scans, COVID-19, lung cancer, brain tumor, and alzheimer parkinson diseases. However, the intricate nature of medical data necessitates robust classification models. This study compares support vector machine (SVM), naïve Bayes, k-nearest neighbors (K-NN), artificial neural networks (ANN), and stochastic gradient descent on multi-class medical datasets, employing data collection, Canny image segmentation, hu moment feature extraction, and oversampling/under-sampling for data balancing. Classification algorithms are assessed via 5-fold cross-validation for accuracy, precision, recall, and F-measure. Results indicate variable model performance depending on datasets and sampling strategies. SVM, K-NN, ANN, and SGD demonstrate superior performance on specific datasets, achieving accuracies between 0.49 to 0.57. Conversely, naïve Bayes exhibits limitations, achieving precision levels of 0.46 to 0.47 on certain datasets. The efficacy of oversampling and under-sampling techniques in improving classification accuracy varies inconsistently. These findings aid medical practitioners and researchers in selecting suitable models for diagnostic applications.
Co-Authors Abdul Rachman Manga’ Achrul Abdullah Adriawan Amrullah Andi Aqris Manrang Andi Muh Afdal Andika Syaputra Aulia Putri Utami Azmi Fauziah Nur Erick Irawadi Alwi Faradibah, Amaliah Farniwati Fattah Fina Febriana Haviluddin Haviluddin Herawati Herawati Herdianti Darwis Huzain Azis Ihwana As’ad Lilis Nur Hayati lilis nurhayati Lokapitasari Belluano, Poetri Lestari Lutfi Budi Ilmawan Manga, Abdul Rachman Muflih Awaluddin Muhammad Akram Muhammad Alif Mubarak B Muhammad Arfah Asis Muhammad Jalil Mutia Nurul Ilmi Nia Kurniati Nia Kurniati Nur Indah, Nur Purnawansyah Purnawansyah Ramdan Satra Ramdaniah Ramdaniah Rezky Anugrah Riska Wulandari Suci Wianingrat H Sugiarti, Sugiarti Zaenal Zaenal Zahrizhal Ali Zulham Jaya Syafar