scholarly journals Electronic Nose for Bladder Cancer Detection

2021 ◽  
Vol 5 (1) ◽  
pp. 22
Author(s):  
Heena Tyagi ◽  
Emma Daulton ◽  
Ayman S. Bannaga ◽  
Ramesh P. Arasaradnam ◽  
James A. Covington
Keyword(s):  
Bladder Cancer ◽  
Logistic Regression ◽  
Random Forest ◽  
Sensitivity And Specificity ◽  
Cancer Detection ◽  
Electronic Nose ◽  
Random Forest Classifier ◽  
Urine Samples ◽  
Sparse Logistic Regression ◽  
High Separation

This study outlines the use of an electronic nose as a method for the detection of VOCs as biomarkers of bladder cancer. Here, an AlphaMOS FOX 4000 electronic nose was used for the analysis of urine samples from 15 bladder cancer and 41 non-cancerous patients. The FOX 4000 consists of 18 MOS sensors that were used to differentiate the two groups. The results obtained were analysed using s MultiSens Analyzer and RStudio. The results showed a high separation with sensitivity and specificity of 0.93 and 0.88, respectively, using a Sparse Logistic Regression and 0.93 and 0.76 using a Random Forest classifier. We conclude that the electronic nose shows potential for discriminating bladder cancer from non-cancer subjects using urine samples.

Label free Impedimetric Immunosensor for effective bladder Cancer detection in clinical urine samples

2020 ◽  
Vol 22 (3) ◽  
Author(s):  
Muhammad Omar Shaikh ◽  
Ting-Chi Huang ◽  
Ting-Feng Wu ◽  
Cheng-Hsin Chuang
Keyword(s):  
Bladder Cancer ◽  
Cancer Detection ◽  
Urine Samples ◽  
Label Free ◽  
Impedimetric Immunosensor

scholarly journals On the Feature Selection of Microarray Data for Cancer Detection based on Random Forest Classifier

2020 ◽  
Vol 12 (3) ◽  
Author(s):  
Tita Nurul Nuklianggraita ◽  
Adiwijaya Adiwijaya ◽  
Annisa Aditsania
Keyword(s):  
Random Forest ◽  
Cancer Detection ◽  
Microarray Data ◽  
Random Forest Classifier ◽  
World Health ◽  
Selection Operator ◽  
Health Organization ◽  
Microarray Technique ◽  
Selection Of ◽  
Better Than

Cancer is a disease that can affect all organs of humans. Based on data from the World Health Organization (WHO) fact sheet in 2018, cancer deaths have reached 9.6 million. One known way to detect cancer that is with Microarray Technique, but the microarray data have large dimensions due to the number of features that are very much compared to the number of samples. Therefore, dimension reduction should be made to produce optimum accuracy. In this paper, we compare Minimum Redundancy Maximum Relevance (MRMR) and Least Absolute Shrinkage and Selection Operator (LASSO) to reduce dimension of microarray data. Moreover, by using Random Forest (RF) Classifier, the performance of classification (cancer detection) is compared. Based on simulation, it can be concluded that LASSO is better than MRMR because it can produce an evaluation of 100% in lung and ovarian cancer, 92% colon cancer, 93% prostate tumor and 83% central nervous system.

“Sniffing bladder cancer” – detection of bladder tumours with an electronic nose and ion mobility spectrometry

2018 ◽  
Vol 17 (2) ◽  
pp. e1427
Author(s):  
H. Heers ◽  
J.M. Gut ◽  
A. Hegele ◽  
R. Hofmann ◽  
T. Boeselt ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Cancer Detection ◽  
Electronic Nose ◽  
Ion Mobility Spectrometry ◽  
Ion Mobility ◽  
Bladder Tumours

scholarly journals EEG Classification Using TQWT and Classifiers

2020 ◽  
Vol 5 (8) ◽  
pp. 603-611
Author(s):  
Sibasankar Padhy ◽  
S Sai Suryateja
Keyword(s):  
Logistic Regression ◽  
Random Forest ◽  
Epileptic Seizures ◽  
Random Forest Classifier ◽  
Intrinsic Mode Functions ◽  
Eeg Signals ◽  
Maximum Accuracy ◽  
Specificity And Sensitivity ◽  
Electroencephalogram Eeg ◽  
Logistic Regression Classifier

The purpose of this study is to detect the epileptic seizures, which can be indicated by the abnormal disturbances in intracranial neurons using the electroencephalogram (EEG) signals. The EEG signals are grouped into three categories viz., Normal EEG signals (Z and O subsets), Seizure-free EEG signals (N and F subsets), and Seizure EEG signals (S subset). Whereas, for classification in this study, EEG signals are divided into three groups namely NF-S, O-FS, and ZO-NF-S. The signal length is fixed to be 4096 samples. The EEG signals will be decomposed by using Tunable-Q Wavelet Transform (TQWT), which produces intrinsic mode functions (IMFs) in decreasing order of frequency. These IMFs are analysed to gather the features of these signals, which help to classify them into various categories, and these features are fed as inputs to three classifiers viz., Random Forest (RF), Decision Table (DT), and Logistic Regression (LR). Logistic Regression classifier has showed higher accuracy, specificity and sensitivity for NF-S and O-F-S groups in comparison to RF and DT classifiers, whereas, Random Forest classifier expressed higher accuracy, specificity and sensitivity for ZO-NF-S groups in comparison to other classifiers. By utilising LR classifier, the suitable parameters of TQWT in NF-S (seizure-free vs. Seizure) are Q=6, r=3, and J=9 and showed maximum accuracy of 98%; and in O-F-S (Normal vs. Seizure-free vs. Seizure), Q=1, r=3, and J=9 attained maximum accuracy of 94.7%. Whereas, in ZONF-S (Normal vs. Seizure-free vs. Seizure), Q=4, r=3, and J=9 expressed maximum accuracy of 99.8% utilising Random Forest classifier.

scholarly journals Improved Non-Invasive Diagnosis of Bladder Cancer with an Electronic Nose: A Large Pilot Study

2021 ◽  
Vol 10 (21) ◽  
pp. 4984
Author(s):  
PierFrancesco Bassi ◽  
Luca Di Gianfrancesco ◽  
Luigi Salmaso ◽  
Mauro Ragonese ◽  
Giuseppe Palermo ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Pilot Study ◽  
Sensitivity And Specificity ◽  
Electronic Nose ◽  
False Negative ◽  
Cost Effective ◽  
Combination Method ◽  
Non Invasive ◽  
Phase 2 Study ◽  
Sensitivity Specificity

Background: Bladder cancer (BCa) emits specific volatile organic compounds (VOCs) in the urine headspace that can be detected by an electronic nose. The diagnostic performance of an electronic nose in detecting BCa was investigated in a pilot study. Methods: A prospective, single-center, controlled, non-randomized, phase 2 study was carried out on 198 consecutive subjects (102 with proven BCa, 96 controls). Urine samples were evaluated with an electronic nose provided with 32 volatile gas analyzer sensors. The tests were repeated at least two times per sample. Accuracy, sensitivity, specificity, and variability were evaluated using mainly the non-parametric combination method, permutation tests, and discriminant analysis classification. Results: Statistically significant differences between BCa patients and controls were reported by 28 (87.5%) of the 32 sensors. The overall discriminatory power, sensitivity, and specificity were 78.8%, 74.1%, and 76%, respectively; 13/96 (13.5%) controls and 29/102 (28.4%) BCa patients were misclassified as false positive and false negative, respectively. Where the most efficient sensors were selected, the sensitivity and specificity increased up to 91.1% (72.5–100) and 89.1% (81–95.8), respectively. None of the tumor characteristics represented independent predictors of device responsiveness. Conclusions: The electronic nose might represent a potentially reliable, quick, accurate, and cost-effective tool for non-invasive BCa diagnosis.

Breast Cancer Detection Using Random Forest Classifier

2021 ◽  
pp. 85-98
Author(s):  
Pavithra Suchindran ◽  
Vanithamani R. ◽  
Judith Justin
Keyword(s):  
Breast Cancer ◽  
Random Forest ◽  
Cancer Detection ◽  
Region Of Interest ◽  
Texture Features ◽  
Speckle Noise ◽  
Random Forest Classifier ◽  
Breast Cancer Detection ◽  
K Nearest Neighbors ◽  
Cad System

Breast cancer is the second most prevalent type of cancer among women. Breast ultrasound (BUS) imaging is one of the most frequently used diagnostic tools to detect and classify abnormalities in the breast. To improve the diagnostic accuracy, computer-aided diagnosis (CAD) system is helpful for breast cancer detection and classification. Normally, a CAD system consists of four stages: pre-processing, segmentation, feature extraction, and classification. In this chapter, the pre-processing step includes speckle noise removal using speckle reducing anisotropic diffusion (SRAD) filter. The goal of segmentation is to locate the region of interest (ROI) and active contour-based segmentation and fuzzy C means segmentation (FCM) are used in this work. The texture features are extracted and fed to a classifier to categorize the images as normal, benign, and malignant. In this work, three classifiers, namely k-nearest neighbors (KNN) algorithm, decision tree algorithm, and random forest classifier, are used and the performance is compared based on the accuracy of classification.

scholarly journals Modeling Road Accident Severity with Comparisons of Logistic Regression, Decision Tree and Random Forest

Information ◽  
2020 ◽  
Vol 11 (5) ◽  
pp. 270 ◽  
Author(s):  
Mu-Ming Chen ◽  
Mu-Chen Chen
Keyword(s):  
Logistic Regression ◽  
Random Forest ◽  
Sensitivity And Specificity ◽  
Prediction Models ◽  
Regression Tree ◽  
Positive Influence ◽  
Road Accident ◽  
Prediction Performance ◽  
Classification And Regression Tree ◽  
Accident Severity

To reduce the damage caused by road accidents, researchers have applied different techniques to explore correlated factors and develop efficient prediction models. The main purpose of this study is to use one statistical and two nonparametric data mining techniques, namely, logistic regression (LR), classification and regression tree (CART), and random forest (RF), to compare their prediction capability, identify the significant variables (identified by LR) and important variables (identified by CART or RF) that are strongly correlated with road accident severity, and distinguish the variables that have significant positive influence on prediction performance. In this study, three prediction performance evaluation measures, accuracy, sensitivity and specificity, are used to find the best integrated method which consists of the most effective prediction model and the input variables that have higher positive influence on accuracy, sensitivity and specificity.

scholarly journals Non-Invasive Diagnosis of Diabetes by Volatile Organic Compounds in Urine Using FAIMS and Fox4000 Electronic Nose

Biosensors ◽  
2018 ◽  
Vol 8 (4) ◽  
pp. 121 ◽  
Author(s):  
Siavash Esfahani ◽  
Alfian Wicaksono ◽  
Ella Mozdiak ◽  
Ramesh Arasaradnam ◽  
James Covington
Keyword(s):  
Volatile Organic Compounds ◽  
Organic Compounds ◽  
Electronic Nose ◽  
Area Under The Curve ◽  
Urine Samples ◽  
Support Vector ◽  
Sparse Logistic Regression ◽  
Non Invasive ◽  
Volatile Organic ◽  
Sample Age

The electronic nose (eNose) is an instrument designed to mimic the human olfactory system. Usage of eNose in medical applications is more popular than ever, due to its low costs and non-invasive nature. The eNose sniffs the gases and vapours that emanate from human waste (urine, breath, and stool) for the diagnosis of variety of diseases. Diabetes mellitus type 2 (DM2) affects 8.3% of adults in the world, with 43% being underdiagnosed, resulting in 4.9 million deaths per year. In this study, we investigated the potential of urinary volatile organic compounds (VOCs) as novel non-invasive diagnostic biomarker for diabetes. In addition, we investigated the influence of sample age on the diagnostic accuracy of urinary VOCs. We analysed 140 urine samples (73 DM2, 67 healthy) with Field-Asymmetric Ion Mobility Spectrometry (FAIMS); a type of eNose; and FOX 4000 (AlphaM.O.S, Toulouse, France). Urine samples were collected at UHCW NHS Trust clinics over 4 years and stored at −80 °C within two hours of collection. Four different classifiers were used for classification, specifically Sparse Logistic Regression, Random Forest, Gaussian Process, and Support Vector on both FAIMS and FOX4000. Both eNoses showed their capability of diagnosing DM2 from controls and the effect of sample age on the discrimination. FAIMS samples were analysed for all samples aged 0–4 years (AUC: 88%, sensitivity: 87%, specificity: 82%) and then sub group samples aged less than a year (AUC (Area Under the Curve): 94%, Sensitivity: 92%, specificity: 100%). FOX4000 samples were analysed for all samples aged 0–4 years (AUC: 85%, sensitivity: 77%, specificity: 85%) and a sub group samples aged less than 18 months: (AUC: 94%, sensitivity: 90%, specificity: 89%). We demonstrated that FAIMS and FOX 4000 eNoses can discriminate DM2 from controls using urinary VOCs. In addition, we showed that urine sample age affects discriminative accuracy.

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