Optical Biopsy for Prostate Cancer Diagnosis Using Fluorescence Spectroscopy

Native fluorescence spectra are acquired from fresh normal and cancerous human prostate tissues. The fluorescence data are analyzed using an unsupervised machine learning algorithm such as non-negative matrix factorization. The nonnegative spectral components are retrieved and attributed to the native fluorophores such as collagen, reduced nicotinamide adenine dinucleotide (NADH), and flavin adenine dinucleotide (FAD) in tissue. The retrieved scores of the components are used to estimate the relative concentrations of the native fluorophores such as NADH and FAD and the redox ratio. A supervised machine learning algorithm such as support vector machine (SVM) is used to classify normal and cancerous tissue samples based on either the relative concentrations of NADH and FAD or the redox ratio alone. Various statistical measures such as sensitivity, specificity, and accuracy, along with the area under receiver operating characteristic (ROC) curve are used to show the classification performance. A cross validation method such as leave-one-out is used to further evaluate the predictive performance of the SVM classifier to avoid bias due to overfitting, and the accuracy was found to be 93.3%.

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An Overview of Supervised Machine Learning Methods and Data Analysis for COVID-19 Detection

Journal of Healthcare Engineering ◽

10.1155/2021/4733167 ◽

2021 ◽

Vol 2021 ◽

pp. 1-18

Author(s):

Aurelle Tchagna Kouanou ◽

Thomas Mih Attia ◽

Cyrille Feudjio ◽

Anges Fleurio Djeumo ◽

Adèle Ngo Mouelas ◽

...

Keyword(s):

Machine Learning ◽

Data Analysis ◽

Learning Algorithm ◽

Machine Learning Algorithms ◽

High Rate ◽

Supervised Machine Learning ◽

Polymerase Chain Reaction Test ◽

Support Vector ◽

Machine Learning Algorithm ◽

Test Results

Background and Objective. To mitigate the spread of the virus responsible for COVID-19, known as SARS-CoV-2, there is an urgent need for massive population testing. Due to the constant shortage of PCR (polymerase chain reaction) test reagents, which are the tests for COVID-19 by excellence, several medical centers have opted for immunological tests to look for the presence of antibodies produced against this virus. However, these tests have a high rate of false positives (positive but actually negative test results) and false negatives (negative but actually positive test results) and are therefore not always reliable. In this paper, we proposed a solution based on Data Analysis and Machine Learning to detect COVID-19 infections. Methods. Our analysis and machine learning algorithm is based on most cited two clinical datasets from the literature: one from San Raffaele Hospital Milan Italia and the other from Hospital Israelita Albert Einstein São Paulo Brasilia. The datasets were processed to select the best features that most influence the target, and it turned out that almost all of them are blood parameters. EDA (Exploratory Data Analysis) methods were applied to the datasets, and a comparative study of supervised machine learning models was done, after which the support vector machine (SVM) was selected as the one with the best performance. Results. SVM being the best performant is used as our proposed supervised machine learning algorithm. An accuracy of 99.29%, sensitivity of 92.79%, and specificity of 100% were obtained with the dataset from Kaggle (https://www.kaggle.com/einsteindata4u/covid19) after applying optimization to SVM. The same procedure and work were performed with the dataset taken from San Raffaele Hospital (https://zenodo.org/record/3886927#.YIluB5AzbMV). Once more, the SVM presented the best performance among other machine learning algorithms, and 92.86%, 93.55%, and 90.91% for accuracy, sensitivity, and specificity, respectively, were obtained. Conclusion. The obtained results, when compared with others from the literature based on these same datasets, are superior, leading us to conclude that our proposed solution is reliable for the COVID-19 diagnosis.

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A Practical Fault Tolerance Approach in Cloud Computing Using Support Vector Machine

10.54646/bijscit.010 ◽

2020 ◽

pp. 45-49

Author(s):

Gajendra Sharma ◽

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Cloud Computing ◽

Fault Tolerance ◽

Learning Algorithm ◽

Supervised Machine Learning ◽

Support Vector ◽

Machine Learning Algorithm ◽

Tolerance Approach ◽

Systematic Solution

Fault tolerance is an important issue in the field of cloud computing which is concerned with the techniques or mechanism needed to enable a system to tolerate the faults that may encounter during its functioning. Fault tolerance policy can be categorized into three categories viz. proactive, reactive and adaptive. Providing a systematic solution the loss can be minimized and guarantee the availability and reliability of the critical services. The purpose and scope of this study is to recommend Support Vector Machine, a supervised machine learning algorithm to proactively monitor the fault so as to increase the availability and reliability by combining the strength of machine learning algorithm with cloud computing.

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Comparing different supervised machine learning algorithms for disease prediction

BMC Medical Informatics and Decision Making ◽

10.1186/s12911-019-1004-8 ◽

2019 ◽

Vol 19 (1) ◽

Cited By ~ 19

Author(s):

Shahadat Uddin ◽

Arif Khan ◽

Md Ekramul Hossain ◽

Mohammad Ali Moni

Keyword(s):

Machine Learning ◽

Learning Algorithm ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Relative Performance ◽

Supervised Machine Learning ◽

Support Vector ◽

Disease Prediction ◽

Machine Learning Algorithm ◽

Different Types

Abstract Background Supervised machine learning algorithms have been a dominant method in the data mining field. Disease prediction using health data has recently shown a potential application area for these methods. This study aims to identify the key trends among different types of supervised machine learning algorithms, and their performance and usage for disease risk prediction. Methods In this study, extensive research efforts were made to identify those studies that applied more than one supervised machine learning algorithm on single disease prediction. Two databases (i.e., Scopus and PubMed) were searched for different types of search items. Thus, we selected 48 articles in total for the comparison among variants supervised machine learning algorithms for disease prediction. Results We found that the Support Vector Machine (SVM) algorithm is applied most frequently (in 29 studies) followed by the Naïve Bayes algorithm (in 23 studies). However, the Random Forest (RF) algorithm showed superior accuracy comparatively. Of the 17 studies where it was applied, RF showed the highest accuracy in 9 of them, i.e., 53%. This was followed by SVM which topped in 41% of the studies it was considered. Conclusion This study provides a wide overview of the relative performance of different variants of supervised machine learning algorithms for disease prediction. This important information of relative performance can be used to aid researchers in the selection of an appropriate supervised machine learning algorithm for their studies.

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The effect of gamma value on support vector machine performance with different kernels

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v10i5.pp5497-5506 ◽

2020 ◽

Vol 10 (5) ◽

pp. 5497

Author(s):

Intisar Shadeed Al-Mejibli ◽

Jwan K. Alwan ◽

Dhafar Hamed Abd

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Learning Algorithm ◽

Kernel Functions ◽

Supervised Machine Learning ◽

Support Vector ◽

Svm Classifier ◽

Machine Performance ◽

Rbf Kernel ◽

The Impact

Currently, the support vector machine (SVM) regarded as one of supervised machine learning algorithm that provides analysis of data for classification and regression. This technique is implemented in many fields such as bioinformatics, face recognition, text and hypertext categorization, generalized predictive control and many other different areas. The performance of SVM is affected by some parameters, which are used in the training phase, and the settings of parameters can have a profound impact on the resulting engine’s implementation. This paper investigated the SVM performance based on value of gamma parameter with used kernels. It studied the impact of gamma value on (SVM) efficiency classifier using different kernels on various datasets descriptions. SVM classifier has been implemented by using Python. The kernel functions that have been investigated are polynomials, radial based function (RBF) and sigmoid. UC irvine machine learning repository is the source of all the used datasets. Generally, the results show uneven effect on the classification accuracy of three kernels on used datasets. The changing of the gamma value taking on consideration the used dataset influences polynomial and sigmoid kernels. While the performance of RBF kernel function is more stable with different values of gamma as its accuracy is slightly changed.

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Use of Patient-Reported Symptoms from an Online Symptom Tracking Tool for Dementia Severity Staging: Development and Validation of a Machine Learning Approach

Journal of Medical Internet Research ◽

10.2196/20840 ◽

2020 ◽

Vol 22 (11) ◽

pp. e20840

Author(s):

Aaqib Shehzad ◽

Kenneth Rockwood ◽

Justin Stanley ◽

Taylor Dunn ◽

Susan E Howlett

Keyword(s):

Machine Learning ◽

Learning Algorithm ◽

Supervised Machine Learning ◽

Support Vector ◽

Machine Learning Algorithm ◽

Severe Dementia ◽

Duration Of Symptoms ◽

Dementia Severity ◽

Moderate Dementia ◽

Patient Reported

Background SymptomGuide Dementia (DGI Clinical Inc) is a publicly available online symptom tracking tool to support caregivers of persons living with dementia. The value of such data are enhanced when the specific dementia stage is identified. Objective We aimed to develop a supervised machine learning algorithm to classify dementia stages based on tracked symptoms. Methods We employed clinical data from 717 people from 3 sources: (1) a memory clinic; (2) long-term care; and (3) an open-label trial of donepezil in vascular and mixed dementia (VASPECT). Symptoms were captured with SymptomGuide Dementia. A clinician classified participants into 4 groups using either the Functional Assessment Staging Test or the Global Deterioration Scale as mild cognitive impairment, mild dementia, moderate dementia, or severe dementia. Individualized symptom profiles from the pooled data were used to train machine learning models to predict dementia severity. Models trained with 6 different machine learning algorithms were compared using nested cross-validation to identify the best performing model. Model performance was assessed using measures of balanced accuracy, precision, recall, Cohen κ, area under the receiver operating characteristic curve (AUROC), and area under the precision-recall curve (AUPRC). The best performing algorithm was used to train a model optimized for balanced accuracy. Results The study population was mostly female (424/717, 59.1%), older adults (mean 77.3 years, SD 10.6, range 40-100) with mild to moderate dementia (332/717, 46.3%). Age, duration of symptoms, 37 unique dementia symptoms, and 10 symptom-derived variables were used to distinguish dementia stages. A model trained with a support vector machine learning algorithm using a one-versus-rest approach showed the best performance. The correct dementia stage was identified with 83% balanced accuracy (Cohen κ=0.81, AUPRC 0.91, AUROC 0.96). The best performance was seen when classifying severe dementia (AUROC 0.99). Conclusions A supervised machine learning algorithm exhibited excellent performance in identifying dementia stages based on dementia symptoms reported in an online environment. This novel dementia staging algorithm can be used to describe dementia stage based on user-reported symptoms. This type of symptom recording offers real-world data that reflect important symptoms in people with dementia.

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Can 1H-Nuclear magnetic resonance (NMR) be used for early detection of hepatocellular cancer (HCC)?

Journal of Clinical Oncology ◽

10.1200/jco.2007.25.18_suppl.15107 ◽

2007 ◽

Vol 25 (18_suppl) ◽

pp. 15107-15107

Author(s):

R. V. Iyer ◽

B. Tennant ◽

M. Ruiz ◽

T. Szyperski ◽

D. Trump ◽

...

Keyword(s):

Machine Learning ◽

Learning Algorithm ◽

Hepatocellular Cancer ◽

Screening Tools ◽

Supervised Machine Learning ◽

Support Vector ◽

Machine Learning Algorithm ◽

Data Set ◽

H Nmr ◽

Kappa Value

15107 Background: HCC is a common and rapidly fatal cancer. Current screening tools are inadequate for identification of potentially curable cases. Our aim was to determine whether H-NMR can identify HCC compared to controls in the woodchuck (WC) model of hepatitis related HCC. Methods: Eastern WCs were bred and inoculated at birth with dilute sera from WCs that are chronic carriers of Woodchuck Hepatitis B Virus (WHV). This resulted in chronic hepatitis in ∼60% animals and all carriers developed HCC by 24–36 months. Serum from 10 chronic WHV carriers with HCC (group 1), 5 WHV carriers with no HCC (group 2) and 15 matched non-infected controls (group 3) was obtained. 45uL serum was diluted with 5uL of D2O containing 27mM formic acid + 0.9% saline. Spectra were collected on a 600 MHz INOVA spectrometer using a CapNMR flow probe with 10uL flow cell at 298K without knowledge of group assignments. The resulting 1D spectra were processed using Nuts from AcornNMR. Results: Principle component analysis and supervised PLS-DA was performed using Simca P+ from Umetrics. Despite general separation of groups, the Q2 value of this model was relatively low (0.20). We trained a Support Vector Machine (SVM) algorithm, a supervised machine-learning algorithm, to learn to identify the groups. Evaluation of the performance of the algorithm using 10-fold validation on the data set achieved a Kappa value of 0.43. This algorithm learnt to identify HCC [0.765 ROC, 0.8 sensitivity, and 0.727 positive predictive value (PPV)] and controls (0.75 ROC, 0.69 sensitivity and 0.73 PPV) but not the WHV carrier group, likely due to the small numbers. In a second analysis of 10 HCC and 15 controls, PLS-DA showed clear separation using three components (Q2= 0.5). The corresponding SVM model showed a kappa value of 0.52 and ROC values of 0.767 for both classes. Conclusions: Our preliminary results indicate that H-NMR spectra alone can be used to distinguish HCC from healthy controls using the machine-learning algorithm for classification. Further validation in a larger cohort of woodchucks is ongoing and confirmation of these preliminary findings would support investigation of this technique as a screening tool in patients at risk for developing HCC. No significant financial relationships to disclose.

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FoRex Trading Using Supervised Machine Learning

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i4.15.23024 ◽

2018 ◽

Vol 7 (4.15) ◽

pp. 400 ◽

Cited By ~ 1

Author(s):

Thuy Nguyen Thi Thu ◽

Vuong Dang Xuan

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Exchange Rate ◽

Learning Algorithm ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Supervised Machine Learning ◽

Support Vector ◽

Machine Learning Algorithm ◽

Machine Learning Model

The exchange rate of each money pair can be predicted by using machine learning algorithm during classification process. With the help of supervised machine learning model, the predicted uptrend or downtrend of FoRex rate might help traders to have right decision on FoRex transactions. The installation of machine learning algorithms in the FoRex trading online market can automatically make the transactions of buying/selling. All the transactions in the experiment are performed by using scripts added-on in transaction application. The capital, profits results of use support vector machine (SVM) models are higher than the normal one (without use of SVM).

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A Predictive Model to Classify Undifferentiated Fever Cases Based on Twenty-Four-Hour Continuous Tympanic Temperature Recording

Journal of Healthcare Engineering ◽

10.1155/2017/5707162 ◽

2017 ◽

Vol 2017 ◽

pp. 1-6 ◽

Cited By ~ 3

Author(s):

Pradeepa H. Dakappa ◽

Keerthana Prasad ◽

Sathish B. Rao ◽

Ganaraja Bolumbu ◽

Gopalkrishna K. Bhat ◽

...

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Dengue Fever ◽

Bacterial Infections ◽

Learning Algorithm ◽

Supervised Machine Learning ◽

Tympanic Temperature ◽

Support Vector ◽

Machine Learning Algorithm ◽

Temperature Recording

Diagnosis of undifferentiated fever is a major challenging task to the physician which often remains undiagnosed and delays the treatment. The aim of the study was to record and analyze a 24-hour continuous tympanic temperature and evaluate its utility in the diagnosis of undifferentiated fevers. This was an observational study conducted in the Kasturba Medical College and Hospitals, Mangaluru, India. A total of ninety-six (n=96) patients were presented with undifferentiated fever. Their tympanic temperature was recorded continuously for 24 hours. Temperature data were preprocessed and various signal characteristic features were extracted and trained in classification machine learning algorithms using MATLAB software. The quadratic support vector machine algorithm yielded an overall accuracy of 71.9% in differentiating the fevers into four major categories, namely, tuberculosis, intracellular bacterial infections, dengue fever, and noninfectious diseases. The area under ROC curve for tuberculosis, intracellular bacterial infections, dengue fever, and noninfectious diseases was found to be 0.961, 0.801, 0.815, and 0.818, respectively. Good agreement was observed [kappa = 0.618 (p<0.001, 95% CI (0.498–0.737))] between the actual diagnosis of cases and the quadratic support vector machine learning algorithm. The 24-hour continuous tympanic temperature recording with supervised machine learning algorithm appears to be a promising noninvasive and reliable diagnostic tool.

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Use of Patient-Reported Symptoms from an Online Symptom Tracking Tool for Dementia Severity Staging: Development and Validation of a Machine Learning Approach (Preprint)

10.2196/preprints.20840 ◽

2020 ◽

Author(s):

Aaqib Shehzad ◽

Kenneth Rockwood ◽

Justin Stanley ◽

Taylor Dunn ◽

Susan E Howlett

Keyword(s):

Machine Learning ◽

Learning Algorithm ◽

Supervised Machine Learning ◽

Support Vector ◽

Machine Learning Algorithm ◽

Severe Dementia ◽

Duration Of Symptoms ◽

Dementia Severity ◽

Moderate Dementia ◽

Patient Reported

BACKGROUND SymptomGuide Dementia (DGI Clinical Inc) is a publicly available online symptom tracking tool to support caregivers of persons living with dementia. The value of such data are enhanced when the specific dementia stage is identified. OBJECTIVE We aimed to develop a supervised machine learning algorithm to classify dementia stages based on tracked symptoms. METHODS We employed clinical data from 717 people from 3 sources: (1) a memory clinic; (2) long-term care; and (3) an open-label trial of donepezil in vascular and mixed dementia (VASPECT). Symptoms were captured with SymptomGuide Dementia. A clinician classified participants into 4 groups using either the Functional Assessment Staging Test or the Global Deterioration Scale as mild cognitive impairment, mild dementia, moderate dementia, or severe dementia. Individualized symptom profiles from the pooled data were used to train machine learning models to predict dementia severity. Models trained with 6 different machine learning algorithms were compared using nested cross-validation to identify the best performing model. Model performance was assessed using measures of balanced accuracy, precision, recall, Cohen κ, area under the receiver operating characteristic curve (AUROC), and area under the precision-recall curve (AUPRC). The best performing algorithm was used to train a model optimized for balanced accuracy. RESULTS The study population was mostly female (424/717, 59.1%), older adults (mean 77.3 years, SD 10.6, range 40-100) with mild to moderate dementia (332/717, 46.3%). Age, duration of symptoms, 37 unique dementia symptoms, and 10 symptom-derived variables were used to distinguish dementia stages. A model trained with a support vector machine learning algorithm using a one-versus-rest approach showed the best performance. The correct dementia stage was identified with 83% balanced accuracy (Cohen κ=0.81, AUPRC 0.91, AUROC 0.96). The best performance was seen when classifying severe dementia (AUROC 0.99). CONCLUSIONS A supervised machine learning algorithm exhibited excellent performance in identifying dementia stages based on dementia symptoms reported in an online environment. This novel dementia staging algorithm can be used to describe dementia stage based on user-reported symptoms. This type of symptom recording offers real-world data that reflect important symptoms in people with dementia.

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