PATH-44. RAMAN SPECTROSCOPY AS A DIAGNOSTIC TOOL IN NEUROPATHOLOGY

Abstract BACKGROUND Although microscopic assessment is still the diagnostic gold standard in pathology, non-light microscopic methods such as new imaging methods and molecular pathology have considerably contributed to more precise diagnostics. As an upcoming method, Raman spectroscopy (RS) offers a "molecular fingerprint" which could be used to differentiate tissue heterogeneity or diagnostic entities. RS has so far been successfully applied on fresh and frozen tissue, however more aggressively, chemically treated tissue such as formalin-fixed, paraffin-embedded (FFPE) samples are challenging for RS. METHODS To address this issue, we examined FFPE samples of a broad range of intracranial tumors (e.g. glioblastoma and primary CNS lymphoma) and also different areas of morphologically highly heterogeneous glioblastoma tumor tissue. The latter in order to classify not only the tumor entity but also histologically defined GBM areas according to their spectral properties. We applied linear and nonlinear machine learning algorithms (Logistic Regression, Random Forest, Support Vector Machine) on our spectroscopic data and compared statistical performance of resulting classifiers. RESULTS We found that Random Forest classification distinguished between glioblastoma and primary CNS lymphoma with a balanced accuracy of 94%, only using Raman measurements on FFPE tissue. Furthermore, our established support vector machine-based classifier identified distinct histological areas in glioblastoma such as tumor core and necroses with an overall accuracy of 70.5% and showed a clear separation between the areas of necrosis and peritumoral zone. CONCLUSIONS This relatively cheap and easy-to-apply tool may serve useful to complement histopathological and molecular diagnostics. It provides an unbiased approach to tumor diagnostics with very little requirements (e.g. histopathological feature completeness of the tumor entity) to the sample. As a conclusion, we propose RS as a potential future additional method in the (neuro)-pathological toolbox for tumor diagnostics.

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Execution Assessment of Machine Learning Algorithms for Spam Profile Detection on Instagram

International Journal of Advanced Trends in Computer Science and Engineering ◽

10.30534/ijatcse/2021/561032021 ◽

2021 ◽

Vol 10 (3) ◽

pp. 1889-1894

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Random Forest ◽

Nearest Neighbor ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Support Vector ◽

Learning Tools ◽

Learning Models ◽

K Nearest Neighbor

Witheverypassingsecondsocialnetworkcommunityisgrowingrapidly,becauseofthat,attackershaveshownkeeninterestinthesekindsofplatformsandwanttodistributemischievouscontentsontheseplatforms.Withthefocus on introducing new set of characteristics and features forcounteractivemeasures,agreatdealofstudieshasresearchedthe possibility of lessening the malicious activities on social medianetworks. This research was to highlight features for identifyingspammers on Instagram and additional features were presentedto improve the performance of different machine learning algorithms. Performance of different machine learning algorithmsnamely, Multilayer Perceptron (MLP), Random Forest (RF), K-Nearest Neighbor (KNN) and Support Vector Machine (SVM)were evaluated on machine learning tools named, RapidMinerand WEKA. The results from this research tells us that RandomForest (RF) outperformed all other selected machine learningalgorithmsonbothselectedmachinelearningtools.OverallRandom Forest (RF) provided best results on RapidMiner. Theseresultsareusefulfortheresearcherswhoarekeentobuildmachine learning models to find out the spamming activities onsocialnetworkcommunities.

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Detecting Real-Time Fall of Elderly People Using Machine Learning

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.39635 ◽

2021 ◽

Vol 9 (12) ◽

pp. 1913-1918

Author(s):

Prathima P

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Random Forest ◽

Elderly People ◽

Fall Detection ◽

Machine Learning Algorithms ◽

Supervised Machine Learning ◽

Support Vector ◽

False Alarms ◽

Severe Injuries

Abstract: Fall is a significant national health issue for the elderly people, generally resulting in severe injuries when the person lies down on the floor over an extended period without any aid after experiencing a great fall. Thus, elders need to be cared very attentively. A supervised-machine learning based fall detection approach with accelerometer, gyroscope is devised. The system can detect falls by grouping different actions as fall or non-fall events and the care taker is alerted immediately as soon as the person falls. The public dataset SisFall with efficient class of features is used to identify fall. The Random Forest (RF) and Support Vector Machine (SVM) machine learning algorithms are employed to detect falls with lesser false alarms. The SVM algorithm obtain a highest accuracy of 99.23% than RF algorithm. Keywords: Fall detection, Machine learning, Supervised classification, Sisfall, Activities of daily living, Wearable sensors, Random Forest, Support Vector Machine

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Prediction of non-muscle invasive bladder cancer recurrence using machine learning of quantitative nuclear features

Modern Pathology ◽

10.1038/s41379-021-00955-y ◽

2021 ◽

Author(s):

Naoto Tokuyama ◽

Akira Saito ◽

Ryu Muraoka ◽

Shuya Matsubara ◽

Takeshi Hashimoto ◽

...

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Bladder Cancer ◽

Random Forest ◽

Machine Learning Algorithms ◽

Invasive Bladder Cancer ◽

Support Vector ◽

Muscle Invasive Bladder Cancer ◽

Nuclear Atypia ◽

Muscle Invasive

AbstractNon-muscle invasive bladder cancer (NMIBC) generally has a good prognosis; however, recurrence after transurethral resection (TUR), the standard primary treatment, is a major problem. Clinical management after TUR has been based on risk classification using clinicopathological factors, but these classifications are not complete. In this study, we attempted to predict early recurrence of NMIBC based on machine learning of quantitative morphological features. In general, structural, cellular, and nuclear atypia are evaluated to determine cancer atypia. However, since it is difficult to accurately quantify structural atypia from TUR specimens, in this study, we used only nuclear atypia and analyzed it using feature extraction followed by classification using Support Vector Machine and Random Forest machine learning algorithms. For the analysis, 125 patients diagnosed with NMIBC were used; data from 95 patients were randomly selected for the training set, and data from 30 patients were randomly selected for the test set. The results showed that the support vector machine-based model predicted recurrence within 2 years after TUR with a probability of 90% and the random forest-based model with probability of 86.7%. In the future, the system can be used to objectively predict NMIBC recurrence after TUR.

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Interpolation of Instantaneous Air Temperature Using Geographical and MODIS Derived Variables with Machine Learning Techniques

10.20944/preprints201906.0008.v1 ◽

2019 ◽

Author(s):

Marcos Ruiz-Álvarez ◽

Francisco Alonso-Sarría ◽

Francisco Gomariz-Castillo

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Random Forest ◽

Linear Regression ◽

Air Temperature ◽

Satellite Data ◽

Multivariate Linear Regression ◽

Machine Learning Algorithms ◽

Machine Learning Techniques ◽

Support Vector

Several methods have been tried to estimate air temperature using satellite imagery. In this paper, the results of two machine learning algorithms, Support Vector Machine and Random Forest, are compared with Multivariate Linear Regression, TVX and Ordinary kriging. Several geographic, remote sensing and time variables are used as predictors. The validation is carried out using four different statistics on a daily basis allowing the use of ANOVA to compare the results. The main conclusion is that Random Forest with residual kriging produces the best results (R$^2$=0.612 $\pm$ 0.019, NSE=0.578 $\pm$ 0.025, RMSE=1.068 $\pm$ 0.027, PBIAS=-0.172 $\pm$ 0.046), whereas TVX produces the least accurate results. The environmental conditions in the study area are not really suited to TVX, moreover this method only takes into account satellite data. On the other hand, regression methods (Support Vector Machine, Random Forest and Multivariate Linear Regression) use several parameters that are easily calculated from a Digital Elevation Model, adding very little difficulty to the use of satellite data alone. The most important variables in the Random Forest Model were satellite temperature, potential irradiation and cdayt, a cosine transformation of the julian day.

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Machine learning in the diagnosis of Myocardial Infarction with Non-Obstructive Coronary Arteries

European Heart Journal ◽

10.1093/eurheartj/ehab724.3067 ◽

2021 ◽

Vol 42 (Supplement_1) ◽

Author(s):

M J Espinosa Pascual ◽

P Vaquero Martinez ◽

V Vaquero Martinez ◽

J Lopez Pais ◽

B Izquierdo Coronel ◽

...

Keyword(s):

Machine Learning ◽

Myocardial Infarction ◽

Support Vector Machine ◽

Logistic Regression ◽

Random Forest ◽

Obstructive Coronary Artery Disease ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Classification Model ◽

Support Vector

Abstract Introduction Out of all patients admitted with Myocardial Infarction, 10 to 15% have Myocardial Infarction with Non-Obstructive Coronaries Arteries (MINOCA). Classification algorithms based on deep learning substantially exceed traditional diagnostic algorithms. Therefore, numerous machine learning models have been proposed as useful tools for the detection of various pathologies, but to date no study has proposed a diagnostic algorithm for MINOCA. Purpose The aim of this study was to estimate the diagnostic accuracy of several automated learning algorithms (Support-Vector Machine [SVM], Random Forest [RF] and Logistic Regression [LR]) to discriminate between people suffering from MINOCA from those with Myocardial Infarction with Obstructive Coronary Artery Disease (MICAD) at the time of admission and before performing a coronary angiography, whether invasive or not. Methods A Diagnostic Test Evaluation study was carried out applying the proposed algorithms to a database constituted by 553 consecutive patients admitted to our Hospital with Myocardial Infarction. According to the definitions of 2016 ESC Position Paper on MINOCA, patients were classified into two groups: MICAD and MINOCA. Out of the total 553 patients, 214 were discarded due to the lack of complete data. The set of machine learning algorithms was trained on 244 patients (training sample: 75%) and tested on 80 patients (test sample: 25%). A total of 64 variables were available for each patient, including demographic, clinical and laboratorial features before the angiographic procedure. Finally, the diagnostic precision of each architecture was taken. Results The most accurate classification model was the Random Forest algorithm (Specificity [Sp] 0.88, Sensitivity [Se] 0.57, Negative Predictive Value [NPV] 0.93, Area Under the Curve [AUC] 0.85 [CI 0.83–0.88]) followed by the standard Logistic Regression (Sp 0.76, Se 0.57, NPV 0.92 AUC 0.74 and Support-Vector Machine (Sp 0.84, Se 0.38, NPV 0.90, AUC 0.78) (see graph). The variables that contributed the most in order to discriminate a MINOCA from a MICAD were the traditional cardiovascular risk factors, biomarkers of myocardial injury, hemoglobin and gender. Results were similar when the 19 patients with Takotsubo syndrome were excluded from the analysis. Conclusion A prediction system for diagnosing MINOCA before performing coronary angiographies was developed using machine learning algorithms. Results show higher accuracy of diagnosing MINOCA than conventional statistical methods. This study supports the potential of machine learning algorithms in clinical cardiology. However, further studies are required in order to validate our results. FUNDunding Acknowledgement Type of funding sources: None. ROC curves of different algorithms

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A Comparative Study on Supervised Machine Learning Algorithms for Copper Recovery Quality Prediction in a Leaching Process

Sensors ◽

10.3390/s21062119 ◽

2021 ◽

Vol 21 (6) ◽

pp. 2119

Author(s):

Victor Flores ◽

Claudio Leiva

Keyword(s):

Neural Network ◽

Machine Learning ◽

Artificial Neural Network ◽

Support Vector Machine ◽

Random Forest ◽

Mining Industry ◽

Machine Learning Algorithms ◽

Copper Recovery ◽

Support Vector ◽

Copper Mining

The copper mining industry is increasingly using artificial intelligence methods to improve copper production processes. Recent studies reveal the use of algorithms, such as Artificial Neural Network, Support Vector Machine, and Random Forest, among others, to develop models for predicting product quality. Other studies compare the predictive models developed with these machine learning algorithms in the mining industry as a whole. However, not many copper mining studies published compare the results of machine learning techniques for copper recovery prediction. This study makes a detailed comparison between three models for predicting copper recovery by leaching, using four datasets resulting from mining operations in Northern Chile. The algorithms used for developing the models were Random Forest, Support Vector Machine, and Artificial Neural Network. To validate these models, four indicators or values of merit were used: accuracy (acc), precision (p), recall (r), and Matthew’s correlation coefficient (mcc). This paper describes the dataset preparation and the refinement of the threshold values used for the predictive variable most influential on the class (the copper recovery). Results show both a precision over 98.50% and also the model with the best behavior between the predicted and the real values. Finally, the obtained models have the following mean values: acc = 0.943, p = 88.47, r = 0.995, and mcc = 0.232. These values are highly competitive when compared with those obtained in similar studies using other approaches in the context.

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Comparison of Machine Learning Algorithms Using WEKA and Sci-Kit Learn in Classifying Online Shopper Intention

JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING ◽

10.31289/jite.v3i1.2599 ◽

2019 ◽

Vol 3 (1) ◽

pp. 58

Author(s):

Yefta Christian

Keyword(s):

Support Vector Machine ◽

Random Forest ◽

Kappa Statistic ◽

Absolute Error ◽

Machine Learning Algorithms ◽

Support Vector ◽

Test Results ◽

Online Shoppers ◽

Online Stores

<p class="8AbstrakBahasaIndonesia"><span>The growth of online stores nowadays is very rapid. This is supported by faster and better internet infrastructure. The increasing growth of online stores makes the competition more difficult in this business field. It is necessary for online stores to have a website or an application that is able to measure and classify consumers’ spending intentions, so that the consumers will have eyes on things on the sites and applications to make purchases eventually. Classification of online shoppers’ intentions can be done by using several algorithms, such as Naïve Bayes, Multi-Layer Perceptron, Support Vector Machine, Random Forest and J48 Decision Trees. In this case, the comparison of algorithms is done with two tools, WEKA and Sci-Kit Learn by comparing the values of F1-Score, accuracy, Kappa Statistic and mean absolute error. There is a difference between the test results using WEKA and Sci-Kit Learn on the Support Vector Machine algorithm. Based on this research, the Random Forest algorithm is the most appropriate algorithm to be used as an algorithm for classifying online shoppers’ intentions.</span></p>

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APLICAÇÃO DE MACHINE LEARNING NA IDENTIFICAÇÃO DE E-MAILS COMO SPAM

Colloquium Exactarum ◽

10.5747/ce.2020.v12.n3.e327 ◽

2021 ◽

Vol 12 (3) ◽

pp. 31-38

Author(s):

Michelle Tais Garcia Furuya ◽

Danielle Elis Garcia Furuya

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Random Forest ◽

Nearest Neighbors ◽

Machine Learning Algorithms ◽

The Other ◽

Support Vector ◽

K Nearest Neighbors ◽

Mail Service ◽

E Mail

The e-mail service is one of the main tools used today and is an example that technology facilitates the exchange of information. On the other hand, one of the biggest obstacles faced by e-mail services is spam, the name given to the unsolicited message received by a user. The machine learning application has been gaining prominence in recent years as an alternative for efficient identification of spam. In this area, different algorithms can be evaluated to identify which one has the best performance. The aim of the study is to identify the ability of machine learning algorithms to correctly classify e-mails and also to identify which algorithm obtained the greatest accuracy. The database used was taken from the Kaggle platform and the data were processed bythe Orange software with four algorithms: Random Forest (RF), K-Nearest Neighbors (KNN), Support Vector Machine (SVM) and Naive Bayes (NB). The division of data in training and testing considers 80% of the data for training and 20% for testing. The results show that Random Forest was the best performing algorithm with 99% accuracy.

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Coronary Illness Prediction Using Random Forest Classifier

10.3233/apc210285 ◽

2021 ◽

Author(s):

Rekha G ◽

Shanthini B ◽

Ranjith Kumar V

Keyword(s):

Support Vector Machine ◽

Random Forest ◽

Decision Tree ◽

Nearest Neighbor ◽

Heart Diseases ◽

Surrogate Data ◽

Machine Learning Algorithms ◽

Support Vector ◽

K Nearest Neighbor ◽

Learning Techniques

Heart diseases or Cardiovascular Diseases (CVDs) are the main cause of death on the planet throughout the most recent years and become the most dangerous disease in India and the entire world. The UCI repository is utilized to calculate the exactness of the AI calculations for foreseeing coronary illness, as k-nearest neighbor, decision tree, linear regression, and support vector machine. Different indications like chest pain, fasting of heartbeat, etc., are referenced. Large datasets, which are not available in medical and clinical research, are required in order to apply deep learning techniques. Surrogate data is generated from Cleveland dataset. The predicted results show that there is an improvement in classification accuracy. Heart disease is one of the most challenging diseases to diagnose as it is the most recognized killer in the present day. Utilizing AI algorithms, this paper gives anticipating coronary illness. Here, we will use the various machine learning algorithms such as Support Vector Machine, Random Forest, KNN, Naive Bayes, Decision Tree and LR.

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Using Machine Learning to Predict Heart Disease

WSEAS TRANSACTIONS ON BIOLOGY AND BIOMEDICINE ◽

10.37394/23208.2022.19.1 ◽

2022 ◽

Vol 19 ◽

pp. 1-9

Author(s):

Nikhil Bora ◽

Sreedevi Gutta ◽

Ahmad Hadaegh

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Heart Disease ◽

Random Forest ◽

Data Science ◽

Learning Algorithm ◽

Machine Learning Algorithms ◽

Machine Learning Techniques ◽

Support Vector ◽

K Nearest Neighbor

Heart Disease has become one of the most leading cause of the death on the planet and it has become most life-threatening disease. The early prediction of the heart disease will help in reducing death rate. Predicting Heart Disease has become one of the most difficult challenges in the medical sector in recent years. As per recent statistics, about one person dies from heart disease every minute. In the realm of healthcare, a massive amount of data was discovered for which the data-science is critical for analyzing this massive amount of data. This paper proposes heart disease prediction using different machine-learning algorithms like logistic regression, naïve bayes, support vector machine, k nearest neighbor (KNN), random forest, extreme gradient boost, etc. These machine learning algorithm techniques we used to predict likelihood of person getting heart disease on the basis of features (such as cholesterol, blood pressure, age, sex, etc. which were extracted from the datasets. In our research we used two separate datasets. The first heart disease dataset we used was collected from very famous UCI machine learning repository which has 303 record instances with 14 different attributes (13 features and one target) and the second dataset that we used was collected from Kaggle website which contained 1190 patient’s record instances with 11 features and one target. This dataset is a combination of 5 popular datasets for heart disease. This study compares the accuracy of various machine learning techniques. In our research, for the first dataset we got the highest accuracy of 92% by Support Vector Machine (SVM). And for the second dataset, Random Forest gave us the highest accuracy of 94.12%. Then, we combined both the datasets which we used in our research for which we got the highest accuracy of 93.31% using Random Forest.

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