Application of support vector machine algorithm for early differential diagnosis of prostate cancer

Akinnuwesi, Boluwaji A.; Olayanju, Kehinde A.; Aribisala, Benjamin S.; Fashoto, Stephen Gbenga; Mbunge, Elliot; Okpeku, Moses; Owate, Patrick

doi:10.1016/j.dsm.2022.10.001

Cited by 28 publications

(8 citation statements)

References 65 publications

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“…For example, the prostate-specific antigen (PSA) test detects prostate cancer. Although the specificity of this test is as high as approximately 87-95%, its sensitivity is much lower, ranging from 33-59% [14]. FT-IR spectroscopy can distinguish cancer samples from non-cancers with high sensitivity, specificity, and accuracy.…”

Section: Resultsmentioning

confidence: 99%

“…Another method used to diagnose prostate cancer early using features is the Support Vector Machine (SVM) algorithm [13]. SVM is used in pre-processing the Prostate cancer dataset to reduce inhomogeneous distributions in the dataset [14]. Rustam et al In a study conducted by SVM, it was concluded that SVM was better at classifying prostate cancer data, especially in terms of accuracy.…”

Section: Chemometric Analysismentioning

confidence: 99%

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Investigation of Fourier Transform Infrared (FT-IR) Spectroscopy and Chemometric Analysis Method as an Alternative Method in the Diagnosis of Prostate Cancer

Şahin

2024

Türk Doğa Ve Fen Dergisi

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Prostate cancer is one of the most common types of cancer in men. It usually grows slowly and may not show obvious symptoms at first. Prostate cancer can be diagnosed by symptoms or by a doctor performing certain tests during routine health checkups. These tests include physical examination, PSA (Prostate Specific Antigen) Test, biopsy, imaging techniques, and Gleason score. In addition, Fourier transform infrared spectroscopy (FT-IR) is an analysis method used for prostate cancer diagnosis. This study aims to demonstrate FT-IR spectroscopy as an alternative method to other diagnostic methods in the diagnosis of prostate cancer. The FT-IR spectroscopy method is used to examine the molecular structure of samples. For prostate cancer diagnosis, FT-IR spectroscopy can be used to identify molecular changes in prostate tissue and identify characteristics by which cancerous cells differ from healthy cells. FT-IR spectroscopy is based on spectral data obtained by exposing samples to infrared radiation. These spectral data are based on properties associated with the movements of the molecules contained in the samples, such as vibration, rotation and bending. Molecular changes caused by diseases such as prostate cancer may be evident in these spectral data. These changes can provide information about the presence or stage of cancerous cells. Data obtained using FT-IR spectroscopy is processed with statistical analysis methods. These analyses are used to identify molecular differences between cancerous and healthy prostate tissues. In this way, FTIR spectroscopy can help obtain sensitive and accurate results in the diagnosis of prostate cancer.

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Section: Resultsmentioning

confidence: 99%

Section: Chemometric Analysismentioning

confidence: 99%

Investigation of Fourier Transform Infrared (FT-IR) Spectroscopy and Chemometric Analysis Method as an Alternative Method in the Diagnosis of Prostate Cancer

Şahin

2024

Türk Doğa Ve Fen Dergisi

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“…SVMs are capable of spotting patterns in medical images that may be challenging for radiologists to see, which can increase the precision and effectiveness of diagnosis. SVMs give radiologists a strong tool to create individualized treatment regimens and enhance patient outcomes (Akinnuwesi et al, 2022; Hu et al, 2011).…”

Section: Applications Of Deep and Machine Learning In Medical Fieldsmentioning

confidence: 99%

Advancements in medical diagnosis and treatment through machine learning: A review

Ahsan,

Khan,

Khan

et al. 2023

Expert Systems

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The aptness of machine learning (ML) to learn from large datasets, discover trends, and make predictions has demonstrated its potential to metamorphose the medical field. Medical data analysis with ML algorithms can improve patient outcomes in terms of both treatment and diagnosis. This paper investigates the numerous possibilities of ML in the medical industries, including radiology, pathology, genomics, and clinical decision‐making. It also goes over the benefits and drawbacks of ML in various sectors as well as the limitations that come with its application. It illustrates the potential advantages of ML, such as better accuracy and efficiency in diagnosis and individualized treatment programs, through a review of previous studies. Lastly, it provides perspectives on prospective advancements and prospects for the discipline. This study also intends to investigate the applications of deep learning (DL) in the medical field. DL algorithms have performed exceptionally satisfactorily in several healthcare‐related fields. The main conclusions of the study are summarized, and their ramifications for the healthcare sector are discussed in this paper's conclusion. This paper intends to contribute to a greater understanding of the prevailing state of the discipline and the possibility for future developments by emphasizing the prospects of these methodologies to alter medical study and clinical practice.

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“…Machine learning, particularly through the implementation of support vector machines (SVMs), has marked a significant step forward in the interpretation of intricate biomedical data, which could be pivotal in addressing the early detection challenges of prostate cancer [5]. While traditional SVMs have demonstrated proficiency in data classification [6][7][8][9], they face significant constraints in scalability and computational efficiency when applied to the expansive and high-dimensional datasets typical in prostate cancer research.…”

Section: Introductionmentioning

confidence: 99%

Quantum Support Vector Machine for Prostate Cancer Detection: A Performance Analysis

Maouaki,

Said,

Bennai

2024

Preprint

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This study addresses the urgent need for improved prostate cancer detection methods by harnessing the power of advanced technological solutions. We introduce the application of Quantum Support Vector Machine (QSVM) to this critical healthcare challenge, showcasing an enhancement in diagnostic performance over the classical Support Vector Machine (SVM) approach. Our study not only outlines the remarkable improvements in diagnostic performance made by QSVM over the classic SVM technique, but it delves into the advancements brought about by the quantum feature map architecture, which has been carefully identified and evaluated, ensuring it aligns seamlessly with the unique characteristics of our prostate cancer dataset. This architecture succeded in creating a distinct feature space, enabling the detection of complex, non-linear patterns in the data. The findings reveal not only a comparable accuracy with classical SVM (92%) but also a 7.14% increase in sensitivity and a notably high F1-Score (93.33%). This study's important combination of quantum computing in medical diagnostics marks a pivotal step forward in cancer detection, offering promising implications for the future of healthcare technology.

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Application of support vector machine algorithm for early differential diagnosis of prostate cancer

Cited by 28 publications

References 65 publications

Investigation of Fourier Transform Infrared (FT-IR) Spectroscopy and Chemometric Analysis Method as an Alternative Method in the Diagnosis of Prostate Cancer

Investigation of Fourier Transform Infrared (FT-IR) Spectroscopy and Chemometric Analysis Method as an Alternative Method in the Diagnosis of Prostate Cancer

Advancements in medical diagnosis and treatment through machine learning: A review

Quantum Support Vector Machine for Prostate Cancer Detection: A Performance Analysis

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