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All Journal Jurnal Riset Informatika Journal Medical Informatics Technology
Fatimah Asmita Rani
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Computer Science & IT Dentistry Engineering Medicine & Pharmacology Public Health

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COMPARATIVE ANALYSIS OF DIMENSIONALITY REDUCTION FOR BREAST CANCER USING MACHINE LEARNING AND DEEP LEARNING Fatimah Asmita Rani; Lufita Marfiana, Duwi
Jurnal Riset Informatika Vol. 7 No. 3 (2025): Juni 2025
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v7i3.375

Abstract

Breast cancer is one of the leading causes of death among women worldwide. Accurate early detection is essential to improve patient survival rates. Therefore, an efficient and optimal detection method is needed. This study presents a comparative analysis between machine learning and deep learning models integrated with various dimensionality reduction techniques to improve the accuracy of breast cancer classification. The dimensionality reduction methods evaluated include Principal Component Analysis (PCA), t-Distributed Stochastic Neighbor Embedding (t-SNE), Independent Component Analysis (ICA), and Linear Discriminant Analysis (LDA). This study uses a dataset from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC), which includes genetic and clinical data of breast cancer patients. Several classification algorithms are used in the evaluation, including Logistic Regression, Support Vector Machines (SVM), and Convolutional Neural Networks (CNN). Model performance is analyzed based on accuracy, precision, recall, and F1-score metrics. The results show that the LDA technique consistently produces better classification performance compared to other dimensionality reduction methods on various Machine Learning and Deep Learning models. The importance of choosing the right dimensionality reduction method in increasing the effectiveness of learning algorithms and more optimal, especially in the context of complex and high-dimensional medical data. The implications of this study can be used to develop a smarter decision support system in breast cancer diagnosis.
Optimization of Melanoma Skin Cancer Detection through Data Magnification, Filter Preprocessing, Image Enhancement, and Convolutional Techniques Fatimah Asmita Rani
Journal Medical Informatics Technology Volume 3 No. 3, September 2025
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v3i3.48

Abstract

Melanoma skin cancer is one of the most aggressive forms of cancer, requiring early detection to improve patient outcomes. This study evaluates three image processing methods—Laplacian, Box Blur, and Edge Detection—used in melanoma detection, analyzing their performance using Mean Squared Error (MSE) and Structural Similarity Index (SSIM) metrics. Among these, Box Blur demonstrated the best overall performance with the lowest average MSE (104.16), indicating minimal distortion in the processed images. Additionally, it achieved the highest SSIM score (0.851), suggesting that it best preserved the structural integrity of the images, making it the most effective in maintaining both quality and important diagnostic details. In contrast, Edge Detection produced the highest MSE (108.02) and a negative SSIM score (-0.016), significantly distorting image structure and making it less suitable for melanoma detection. Laplacian, while moderate in performance, did not outperform Box Blur, with an MSE of 106.99 and an SSIM of 0.175. These results highlight Box Blur as the most reliable technique for melanoma image analysis, ensuring both clarity and structural preservation. By effectively enhancing diagnostic features and reducing errors, Box Blur offers a valuable tool for clinicians aiming to improve diagnostic accuracy in melanoma detection.
Co-Authors Lufita Marfiana, Duwi