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All Journal Jurnal Riset Informatika
Fatimah Asmita Rani
Universitas Nusa Mandiri
Computer Science & IT

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Optimizing Deep Learning with Dimensionality Reduction for Analyzing the CuMiDa Brain Cancer Gene Expression Dataset Duwi Lufita Marfiana; Fatimah Asmita Rani
Jurnal Riset Informatika Vol. 6 No. 4 (2024): September 2024
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v6i4.350

Abstract

In the digital era, machine learning and deep learning have become indispensable tools for bioinformatics, particularly in analyzing high-dimensional gene expression data for cancer diagnosis and classification. This study leverages the CuMiDa brain cancer dataset, a curated microarray database with 54,676 genes and 130 samples, to evaluate the effectiveness of deep learning models integrated with dimensionality reduction techniques. Principal Component Analysis (PCA) and Truncated Singular Value Decomposition (TruncatedSVD) were employed to address the challenges of high-dimensional data, reducing noise and computational complexity. Three deep learning models—DNN, MLP, and TabNet—were implemented with various optimizers, including ADAM, RMSprop, and SGD. Results showed that TruncatedSVD outperformed PCA in minimizing loss, especially for MLP with LBFGS optimizers, achieving near-zero loss. TabNet demonstrated the highest classification accuracy (96%) with ADAM and RMSprop. Conversely, SGD exhibited suboptimal performance across models. These findings highlight the critical role of dimensionality reduction and optimizer selection in enhancing the efficiency and accuracy of deep learning models for cancer classification. This research provides a robust framework for improving diagnostic tools in computational oncology.
COMPARATIVE ANALYSIS OF DIMENSIONALITY REDUCTION FOR BREAST CANCER USING MACHINE LEARNING AND DEEP LEARNING Fatimah Asmita Rani; Duwi Lufita Marfiana
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.
Co-Authors Duwi Lufita Marfiana Duwi Lufita Marfiana