A comparative study of machine learning algorithms for thyroid disease classification

Machine learning today becomes more and more effective instrument to solve many particular problems, where there are difficulties to apply well known and described math model. In other words - it is a great tool to describe nonlinear phenomena. We tried to use this technique to improve existing process of stratigraphy and lithology interpretation and reduce costs on site by applying computer leaded predictions on the basis of existing on-field collected data. Article describes usage of machine learning algorithms for several geology data stratigraphy and lithology boundaries classification based on geophysics logging data for deposits in Kazakhstan. Correct marking of stratigraphy and lithology from geophysics logging data is complex non-linear task. To solve this task we applied several algorithms of machine learning: random forest, logistic regression, gradient boosting (scikit-learn library), k - nearest neighbour (KNN) and XGBoost.

Despite the promise for performance and accuracy improvements of quantum inspired (QI) algorithms over classical machine learning (ML) algorithms, such gains have not been realized in practice. The quantum inspired algorithms can theoretically achieve significant speed up based on sampling assumptions and have thus far failed to outperform the existing classical ML models in practical applications. The speedup of quantum machine learning (QML) algorithms assume the access to data in quantum random access memory (QRAM) which is a strong assumption with current quantum architectures. QI algorithms assume sample and query (SQ) access to input vector and norms of matrices using a dynamic data structure. We explore the components of these models and the assumptions in this paper by surveying the recent works in QML and QI Machine learning (QIML) algorithms. We limit our study to QML and QIML models on achieving a speed up over classical ML techniques rather than individual proofs of these algorithms. This study highlights the assumptions being made that are currently not practical for QML and QIML algorithms in achieving performance advantage over classical ML algorithms.

In this work, we studied the use of Quantum Machine Learning (QML) algorithms for binary classification and compared their performance with classical Machine Learning (ML) methods. QML merges principles of Quantum Computing (QC) and ML, offering improved efficiency and potential quantum advantage in data-driven tasks and when solving complex problems. In binary classification, where the goal is to assign data to one of two categories, QML uses quantum algorithms to process large datasets efficiently. Quantum algorithms like Quantum Support Vector Machines (QSVM) and Quantum Neural Networks (QNN) exploit quantum parallelism and entanglement to enhance performance over classical methods. This study focuses on two common QML algorithms, Quantum Support Vector Classifier (QSVC) and QNN. We used the Qiskit software and conducted the experiments with three different datasets. Data preprocessing included dimensionality reduction using Principal Component Analysis (PCA) and standardization using scalers. The results showed that quantum algorithms demonstrated competitive performance against their classical counterparts in terms of accuracy, while QSVC performed better than QNN. These findings suggest that QML holds potential for improving computational efficiency in binary classification tasks. This opens the way for more efficient and scalable solutions in complex classification challenges and shows the complementary role of quantum computing.

Machine Learning Algorithms for Stratigraphy Classification on Uranium Deposits

Advancements in the implementation of quantum hardware have enabled the acquisition of data that are intractable for emulation with classical computers. The integration of classical machine learning (ML) algorithms with these data holds potential for unveiling obscure patterns. Although this hybrid approach extends the class of efficiently solvable problems compared to using only classical computers, this approach has been only realized for solving restricted problems because of the prevalence of noise in current quantum computers. Here, we extend the applicability of the hybrid approach to problems of interest in many-body physics, such as predicting the properties of the ground state of a given Hamiltonian and classifying quantum phases. By performing experiments with various error-reducing procedures on superconducting quantum hardware with 127 qubits, we managed to acquire refined data from the quantum computer. This enabled us to demonstrate the successful implementation of theoretically suggested classical ML algorithms for systems with up to 44 qubits. Our results verify the scalability and effectiveness of the classical ML algorithms for processing quantum experimental data.

BackgroundDiffusion-weighted (DW) imaging is a well-recognized magnetic resonance imaging (MRI) technique that is being routinely used in brain examinations in modern clinical radiology practices. This study focuses on extracting demographic and texture features from MRI Apparent Diffusion Coefficient (ADC) images of human brain tumors, identifying the distribution patterns of each feature and applying Machine Learning (ML) techniques to differentiate malignant from benign brain tumors.MethodsThis prospective study was carried out using 1599 labeled MRI brain ADC image slices, 995 malignant, 604 benign from 195 patients who were radiologically diagnosed and histopathologically confirmed as brain tumor patients. The demographics, mean pixel values, skewness, kurtosis, features of Grey Level Co-occurrence Matrix (GLCM), mean, variance, energy, entropy, contrast, homogeneity, correlation, prominence and shade, were extracted from MRI ADC images of each patient. At the feature selection phase, the validity of the extracted features were measured using ANOVA f-test. Then, these features were used as input to several Machine Learning classification algorithms and the respective models were assessed.ResultsAccording to the results of ANOVA f-test feature selection process, two attributes: skewness (3.34) and GLCM homogeneity (3.45) scored the lowest ANOVA f-test scores. Therefore, both features were excluded in continuation of the experiment. From the different tested ML algorithms, the Random Forest classifier was chosen to build the final ML model, since it presented the highest accuracy. The final model was able to predict malignant and benign neoplasms with an 90.41% accuracy after the hyper parameter tuning process.ConclusionsThis study concludes that the above mentioned features (except skewness and GLCM homogeneity) are informative to identify and differentiate malignant from benign brain tumors. Moreover, they enable the development of a high-performance ML model that has the ability to assist in the decision-making steps of brain tumor diagnosis process, prior to attempting invasive diagnostic procedures, such as brain biopsies.

Machine learning (ML) is the most common technique for predicting the future or for classifying information, to help people make the required decisions. ML techniques are based on algorithms – sets of mathematical procedures that explain the relationship between variables. ML algorithms are trained in situations where they learn from past data and can even evaluate historical data. After extensive training, the algorithm can recognize patterns sufficiently to make predictions. ML provides methods, techniques, and tools that can help in solving diagnostic and prognostic problems in a variety of medical domains. It is being used for the analysis of the importance of clinical parameters and their combinations for prognosis, e.g., for prediction of disease progression, for the extraction of medical information to predict research outcomes, for therapy planning and support, and for overall patient management. ML is also being used for data analysis, such as detection of patterns in the data by dealing appropriately with imperfect data, interpretation of continuous data generated in the Intensive Care Unit, and for intelligent alarms, resulting in effective and efficient monitoring. Successful implementation of ML methods can help the integration of computer-based systems into the healthcare environment, providing opportunities to facilitate and enhance the work of medical experts, and ultimately to improve the efficiency and quality of medical care. Most ML methods can be categorized into one of two types of learning techniques, namely supervised or unsupervised algorithms. A supervised learning algorithm takes a known set of input data (the training set) and known responses to the data (the output) and trains a model to generate reasonable predictions for the response to new input data. In medicine, supervised learning involves training a model to relate a person’s characteristics (e.g., height, weight, smoking status) to a certain outcome (onset of diabetes within five years, for example). Once the algorithm is successfully trained, it will be capable of making outcome predictions when supplied with new data. Predictions which are made by models trained using supervised learning can be either discrete (e.g., positive or negative, benign or malignant) or continuous (e.g., a score from 0 to 100). A supervised model which produces discrete categories (sometimes referred to as classes) is referred to as a classification algorithm. Examples of classification algorithms include those which predict whether a tumor is benign or malignant, or to establish whether comments written by a patient convey a positive or negative sentiment. In practice, classification algorithms return the probability of a class (between 0 for impossible and 1 for certain). Common classification algorithms are Logistic Regression, K-Nearest Neighbor, Support Vector Machine, Neural Network, and Naïve Bayes. A supervised model which returns a prediction of a continuous value is known as a regression algorithm, which might be used by ML to predict an individual’s life expectancy or the tolerable dose of chemotherapy. Supervised ML algorithms are typically developed using a dataset, which contains several variables and a relevant outcome. In contrast with supervised learning, unsupervised learning does not involve a predefined outcome. In unsupervised learning, patterns are sought by algorithms without any input from the user. Unsupervised techniques are thus exploratory and used to find undefined patterns or clusters which occur within datasets. In this chapter, the use of different types of supervised and unsupervised ML algorithms for various applications in health care are discussed.

6 - Machine learning and deep learning algorithms for fault diagnosis of photovoltaic systems

Parkinson Disease (PD) is a complex neurological disorder attributed by loss of neurons generating dopamine in the SN per compacta. Electroencephalogram (EEG) plays an important role in diagnosing PD as it offers a non-invasive continuous assessment of the disease progression and reflects these complex patterns. This study focuses on the non-linear analysis of resting state EEG signals in PD, with a gender-specific, brain region-specific, and EEG band-specific approach, utilizing recurrence plots (RPs) and machine learning (ML) algorithms for classification. For this an open EEG dataset consisting of 14 PD and 14 healthy (HC) subjects is utilized. Recurrence plots and cross-recurrence plots (CRPs) were constructed for each frequency band and brain region, extracting complexity measures such as determinism (DET) and entropy (ENT). The interpretability of the ML model decisions is investigated using explainability technique. The scattered distribution of points in RPs of male PD individuals reflects the complex and dynamic nature of abnormal brain function. Also, CRPs confirms the enhanced effect of Beta Gamma synchronization during PD in the Parietal region. Low DET and high ENT corresponds to the complex non-linear characteristics of EEG signals and brain neuronal circuits during PD condition in male subjects. The extracted recurrence features served as inputs to the ML models, which achieved high classification performance, across all the scenarios. This study demonstrates the potential of recurrence plot-based complexity analysis combined with machine learning for the gender-specific, region-specific, and band-specific assessment of EEG signals during resting state in Parkinson's disease.

Network traffic classification is the process of analyzing traffic flows and associating them to different categories of network applications. Network traffic classification represents an essential task in the whole chain of network security. Some of the most important and widely spread applications of traffic classification are the ability to classify encrypted traffic, the identification of malicious traffic flows, and the enforcement of security policies on the use of different applications. Passively monitoring a network utilizing low-cost and low-complexity wireless local area network (WLAN) devices is desirable. Mobile devices can be used or existing office desktops can be temporarily utilized when their computational load is low. This reduces the burden on existing network hardware. The aim of this paper is to investigate traffic classification techniques for wireless communications. To aid with intrusion detection, the key goal is to passively monitor and classify different traffic types over WLAN to ensure that network security policies are adhered to. The classification of encrypted WLAN data poses some unique challenges not normally encountered in wired traffic. WLAN traffic is analyzed for features that are then used as an input to six different machine learning (ML) algorithms for traffic classification. One of these algorithms (a Gaussian mixture model incorporating a universal background model) has not been applied to wired or wireless network classification before. The authors also propose a ML algorithm that makes use of the well-known vector quantization algorithm in conjunction with a decision tree—referred to as a TRee Adaptive Parallel Vector Quantiser. This algorithm has a number of advantages over the other ML algorithms tested and is suited to wireless traffic classification. An average F-score (harmonic mean of precision and recall) > 0.84 was achieved when training and testing on the same day across six distinct traffic types.

System modeling (identification) in complex systems like kinesiological and biological in general is extremely difficult due to the high dimensions of parameters and usually non-linear functional dependencies. Data Science and especially Machine Learning (Deep Learning) algorithms seem to be quite a good tool for analysis and problem-solving in sports today. Data Science (Machine or Deep Learning) algorithms rely on basic use of statistical algorithms, but extend those with models such as Decision tree, K-means clustering, Neural networks, and Reinforcement learning, creating new algorithms that handle input data by predicting outputs that describe correlation relations or predict future states at time points (regression). This study is an attempt to analyze and research applications of machine learning in Sport science - Kinanthropometry related problem of determining somatotype by using the Microsoft Azure Machine Learning platform and comparing several supervised classifier algorithms (Multiclass Neural Network, Multiclass Decision Forest, Multiclass Decision Jungle and Multiclass Logistic Regression) which were compared versus classical somatotype categorization algorithms with dataset based on the Heath-Carter method Somatotype determination to gain experience and expertise.

Investigating the potential of combining Raman spectroscopy and machine learning for gestational age classification in rat cervical tissue.

to predict vestibular schwannoma (VS) response to radiosurgery by applying machine learning (ML) algorithms on radiomic features extracted from pre-treatment magnetic resonance (MR) images. patients with VS treated with radiosurgery in two Centers from 2004 to 2016 were retrospectively evaluated. Brain T1-weighted contrast-enhanced MR images were acquired before and at 24 and 36 months after treatment. Clinical and treatment data were collected contextually. Treatment responses were assessed considering the VS volume variation based on pre- and post-radiosurgery MR images at both time points. Tumors were semi-automatically segmented and radiomic features were extracted. Four ML algorithms (Random Forest, Support Vector Machine, Neural Network, and extreme Gradient Boosting) were trained and tested for treatment response (i.e., increased or non-increased tumor volume) using nested cross-validation. For training, feature selection was performed using the Least Absolute Shrinkage and Selection Operator, and the selected features were used as input to separately build the four ML classification algorithms. To overcome class imbalance during training, Synthetic Minority Oversampling Technique was used. Finally, trained models were tested on the corresponding held out set of patients to evaluate balanced accuracy, sensitivity, and specificity. 108 patients treated with Cyberknife® were retrieved; an increased tumor volume was observed at 24 months in 12 patients, and at 36 months in another group of 12 patients. The Neural Network was the best predictive algorithm for response at 24 (balanced accuracy 73% ± 18%, specificity 85% ± 12%, sensitivity 60% ± 42%) and 36 months (balanced accuracy 65% ± 12%, specificity 83% ± 9%, sensitivity 47% ± 27%). radiomics may predict VS response to radiosurgery avoiding long-term follow-up as well as unnecessary treatment.

Integration of machine learning algorithms in fashion design classification brought transformational change by allowing the automated analysis, categorization, and prediction of fashion items based on different attributes of the item. This paper reviews the state-of-the-art in fashion design classification through machine learning techniques. Review of literature, methodology, and challenges in this area indicate that an array of algorithms and methods, stretching from traditional machine learning algorithms to convolutional neural networks and further to transfer learning approaches, is being tried and tested. In this paper, I will discuss performance comparison among several machine learning algorithms, pinning their strengths, limitations, and possible applications in the fashion industry. We further elaborate on crucial challenges, such as touching on the issue of data variability, interpretability, and others on ethical consideration issues, all pointing to the need for fairness and sustainability with respect to the representation of reality in algorithmic decision-making. This paper aims to inform researchers, practitioners, and stakeholders of the opportunities and challenges brought about by the use of machine learning in a fast-paced world like fashion, hence demystifying current directions in landscape classification of fashion design.

Performance evaluation of machine learning techniques in predicting cumulative absolute velocity