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Efficient Machine Learning Algorithms for Identifying Risk Factors of Prostate and Breast Cancers among Males and Females
Title
Efficient Machine Learning Algorithms for Identifying Risk Factors of Prostate and Breast Cancers among Males and Females.
Creator
Rikhtehgaran, Samaneh, Muhammad, Wazir, Florida Atlantic University, Department of Physics, Charles E. Schmidt College of Science
Abstract/Description
One of the most common types of cancer among women is breast cancer. It represents one of the diseases leading to a high number of mortalities among women. On the other hand, prostate cancer is the second most frequent malignancy in men worldwide. The early detection of prostate cancer is fundamental to reduce mortality and increase the survival rate. A comparison between six types of machine learning models as Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, k Nearest...
Show moreOne of the most common types of cancer among women is breast cancer. It represents one of the diseases leading to a high number of mortalities among women. On the other hand, prostate cancer is the second most frequent malignancy in men worldwide. The early detection of prostate cancer is fundamental to reduce mortality and increase the survival rate. A comparison between six types of machine learning models as Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, k Nearest Neighbors, and Naïve Bayes has been performed. This research aims to identify the most efficient machine learning algorithms for identifying the most significant risk factors of prostate and breast cancers. For this reason, National Health Interview Survey (NHIS) and Prostate, Lung, Colorectal, and Ovarian (PLCO) datasets are used. A comprehensive comparison of risk factors leading to these two crucial cancers can significantly impact early detection and progressive improvement in survival.
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Date Issued
2021
PURL
http://purl.flvc.org/fau/fd/FA00013755
Subject Headings
Machine learning, Algorithms, Cancer--Risk factors, Breast--Cancer, Prostate--Cancer
Format
Document (PDF)
Microfluidic Electrical Impedance Spectroscopy for Blood Analysis
Title
Microfluidic Electrical Impedance Spectroscopy for Blood Analysis.
Creator
Rikhtehgaran, Samaneh, Wille, Luc T., Du, E., Florida Atlantic University, Department of Physics, Charles E. Schmidt College of Science
Abstract/Description
The study of the electrical properties of red blood cells (RBCs) plays a crucial role in advancing our understanding of human health. As RBCs age, they undergo changes that affect hemorheology and blood microcirculation, which have far-reaching implications for disease research. Furthermore, the shortage of RBC storage units can be a major issue for patients, underscoring the importance of characterizing RBC aging with respect to cell densities. In individuals with abnormal hemoglobin disease...
Show moreThe study of the electrical properties of red blood cells (RBCs) plays a crucial role in advancing our understanding of human health. As RBCs age, they undergo changes that affect hemorheology and blood microcirculation, which have far-reaching implications for disease research. Furthermore, the shortage of RBC storage units can be a major issue for patients, underscoring the importance of characterizing RBC aging with respect to cell densities. In individuals with abnormal hemoglobin disease, alterations in hemoglobin and its functionality can modify the volume and density of RBCs, making their study even more crucial. To this end, our aim is to investigate the impedance alterations of RBCs after distributing them into different layers based on their densities. We have developed a novel method for non-invasive, rapid, and real-time single-cell analysis of RBCs. Our approach involves the use of electrical impedance spectroscopy (EIS) to study the cells after performing cell fractionation. Our studies indicate an increasing trend for RBC resistance and a decreasing trend for the cell membrane as the density of the layer increases. Additionally, we have developed a method for extracting hemoglobin with high purity from fresh samples of RBCs. By passing lysed RBCs through ultrafiltration devices and removing debris and membranes, we were able to isolate hemoglobin. Using the EIS technique, we studied the alterations of impedance over a frequency range, obtaining valuable insight into the electrical properties of hemoglobin.
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Date Issued
2023
PURL
http://purl.flvc.org/fau/fd/FA00014223
Subject Headings
Blood--Analysis, Erythrocytes--Aging, Hemorheology, Electrical impedance spectroscopy, Microfluidics
Format
Document (PDF)