Performance Comparison of Machine Learning Techniques in Identifying Dementia from Open Access Clinical Datasets

PurposeThis study aims to emphasize utilization of Predictive Six Sigma to achieve process improvements based on machine learning (ML) techniques embedded in define, measure, analyze, improve, control (DMAIC). With this aim, this study presents selection and utilization of ML techniques, including multiple linear regression (MLR), artificial neural network (ANN), random forests (RF), gradient boosting machines (GBM) and k-nearest neighbors (k-NN) in the analyze and improve phases of Six Sigma DMAIC.Design/methodology/approachA data set containing 320 observations with nine input and one output variables is used. To achieve the objective which was to decrease the number of fabric defects, five ML techniques were compared in terms of prediction performance and best tools were selected. Next, most important causes of defects were determined via these tools. Finally, parameter optimization was conducted for minimum number of defects.FindingsAmong five ML tools, ANN, GBM and RF are found to be the best predictors. Out of nine potential causes, “machine speed” and “fabric width” are determined as the most important variables by using these tools. Then, optimum values for “machine speed” and “fabric width” for fabric defect minimization are determined both via regression response optimizer and ANN surface optimization. Ultimately, average defect number was decreased from 13/roll to 3/roll, which is a considerable decrease attained through utilization of ML techniques in Six Sigma.Originality/valueAddressing an important gap in Six Sigma literature, in this study, certain ML techniques (i.e. MLR, ANN, RF, GBM and k-NN) are compared and the ones possessing best performances are used in the analyze and improve phases of Six Sigma DMAIC.

With the rapid developments in medical technologies and computers, it is getting easier to detect neurological disorders. However, the early detection of Epilepsy which is a common and important neurological disorder is still a crucial and challenging procedure. Like the other disorders, early detection gives some chances for controlling and smoothing of the effects of the disease. Developing early detection methods for epileptic seizures from Electroencephalography (EEG) signals using signal processing and machine learning techniques has been a popular research area in last decades. In this work, we propose to use some time-domain features together with same features extracted for sub-signals of the original signal. For obtaining sub-signals from EEG signal, we used classical discrete-time wavelet decomposition. All EEG signals have been decomposed to five important sub-bands. After extracting features from EEG signals and all sub-signals, we have applied the feature matrices to some machine learning algorithms. Most of the researches in this area distinguish only two or three states of epileptic seizures. In contrary to majority, we preferred to classify all five classes from EEG signals. Classification results of our method show an important success comparing with the other researches. Using statistical, auto-correlation based and wavelet transform based features of EEG signals together to predict and detect epilepsy makes our method robust. In this work, we present the comparison of machine learning techniques for the detection of epilepsy using electroencephalography (EEG) signals. The five states of epilepsy are classified using Naive Bayes (NB), Decision Tree (DT), Random Forest (RF), Multilayer Perceptrons (MLP), k-Nearest Neighbors (k-NN), and Support Vector Machines (SVM) algorithms. Results show that Random Forest, Multilayer Perceptrons, k-Nearest Neighbors (k-NN) with 10-fold cross-validation are the most successful algorithms with 92% accuracy.

Machine Learning (ML) techniques are pervasive tools in various emerging commercial applications, but have to be accommodated by powerful computer systems to process very large data. Although general-purpose CPUs and GPUs have provided straightforward solutions, their energy-efficiencies are limited due to their excessive supports for flexibility. Hardware accelerators may achieve better energy-efficiencies, but each accelerator often accommodates only a single ML technique (family). According to the famous No-Free-Lunch theorem in the ML domain, however, an ML technique performs well on a dataset may perform poorly on another dataset, which implies that such accelerator may sometimes lead to poor learning accuracy. Even if regardless of the learning accuracy, such accelerator can still become inapplicable simply because the concrete ML task is altered, or the user chooses another ML technique.In this study, we present an ML accelerator called PuDianNao, which accommodates seven representative ML techniques, including k-means, k-nearest neighbors, naive bayes, support vector machine, linear regression, classification tree, and deep neural network. Benefited from our thorough analysis on computational primitives and locality properties of different ML techniques, PuDianNao can perform up to 1056 GOP/s (e.g., additions and multiplications) in an area of 3.51 mm^2, and consumes 596 mW only. Compared with the NVIDIA K20M GPU (28nm process), PuDianNao (65nm process) is 1.20x faster, and can reduce the energy by 128.41x.

ObjectiveEnhanced technology in computer and internet has driven scale and quality of data to be improved in various areas including healthcare sectors. Machine Learning (ML) has played a pivotal role in efficiently analyzing those big data, but a general misunderstanding of ML algorithms still exists in applying them (e.g., ML techniques can settle a problem of small sample size, or deep learning is the ML algorithm). This paper reviewed the research of diagnosing mental illness using ML algorithm and suggests how ML techniques can be employed and worked in practice.MethodsResearches about mental illness diagnostic using ML techniques were carefully reviewed. Five traditional ML algorithms-Support Vector Machines (SVM), Gradient Boosting Machine (GBM), Random Forest, Naïve Bayes, and K-Nearest Neighborhood (KNN)-frequently used for mental health area researches were systematically organized and summarized.ResultsBased on literature review, it turned out that Support Vector Machines (SVM), Gradient Boosting Machine (GBM), Random Forest, Naïve Bayes, and K-Nearest Neighborhood (KNN) were frequently employed in mental health area, but many researchers did not clarify the reason for using their ML algorithm though every ML algorithm has its own advantages. In addition, there were several studies to apply ML algorithms without fully understanding the data characteristics.ConclusionResearchers using ML algorithms should be aware of the properties of their ML algorithms and the limitation of the results they obtained under restricted data conditions. This paper provides useful information of the properties and limitation of each ML algorithm in the practice of mental health.

Diabetes is a major chronic syndrome caused by a series of metabolic abnormalities in which blood glucose levels are abnormally high for an indeterminate amount of time. It influences various organs in the human body, resulting in a variety of complex diseases such as stroke, renal disease, pulmonary embolism, eyesight, and so on. Diabetes Disorders (DD) are presently one of the healthcare top causes of mortality. Predictive analytics in the health care system is a huge obstacle, but if accurate early prediction is achieved, the potential risk and degree of diabetes may be significantly decreased. Machine learning (ML) techniques are now used to analyze medical datasets at an earlier stage of life in keeping people safe. In this research, we utilized several ML approaches notably Logistic Regression, Decision Tree (DT), XGBoost, Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Random Forest (RF) on PIMA Indian Diabetes Dataset in order to monitor and evaluate their performances in diabetes prediction. The performance of the various ML algorithms employed in this research suggests which algorithm is most suitable in diabetes prediction. It is observed that among all the models XGBoost had outperformed the other ML techniques with an accuracy of 80.73% while SVM was the second-best performing model with a classification accuracy of 80.21%. Thus, employing ML techniques, this study aims to assist doctors as well as clinicians in the early detection of diabetes.

Cardiovascular diseases (CVDs) have become increasingly prevalent and severe worldwide, necessitating a transition to more advanced and precise predictive models. Traditional approaches to the prognosis of coronary heart disease often lead to higher morbidity and mortality due to delayed early detection and inadequate individualized analysis. Machine learning (ML) techniques offer a groundbreaking solution, capable of analyzing vast amounts of patient data to provide early and accurate predictions of cardiac diseases. This shift holds great promise across various domains, including heart imaging, cardiothoracic surgery, cardiovascular anesthesiology, coronary critical care, pediatric cardiovascular disease, predictive cardiology, and interventional cardiology. In this comprehensive review, we evaluate several ML techniques, specifically focusing on classification and prediction algorithms, to assess their effectiveness in predicting cardiovascular diseases. We examine a range of algorithms such as K-Nearest Neighbors (K-NN), Decision Trees (DT), Support Vector Machines (SVM), Random Forests, Logistic Regression, XGBoost, Extra Trees Classifier, Gaussian Naive Bayes, and Artificial Neural Networks. By comparing these models, we aim to identify the most reliable and effective predictive tools for cardiovascular diseases. Notably, algorithms like Decision Trees and Extra Trees Classifiers have demonstrated remarkable performance, with recorded accuracies of 93.19% and 98.15%, respectively. This study underscores the importance of implementing machine learning approaches to enhance the accuracy and predictive capabilities in cardiac care. By leveraging these models, we can significantly improve early diagnosis, leading to better patient outcomes across cardiology specialties.

ABSTRACTThis paper reviews the activity-travel behaviour literature that employs Machine Learning (ML) techniques for empirical analysis and modelling. Machine Learning algorithms, which attempt to build intelligence utilizing the availability of large amounts of data, have emerged as powerful tools in the fields of pattern recognition and big data analysis. These techniques have been applied in activity-travel behaviour studies since the early ’90s when Artificial Neural Networks (ANN) were employed to model mode choice decisions. AMOS, an activity-based modelling system developed in the mid-’90s, has ANN at its core to model and predict individual responses to travel demand management measures. In the dawn of 2000, ALBATROSS, a comprehensive activity-based travel demand modelling system, was proposed by Arentze and Timmermans using Decision Trees. Since then researchers have been exploring ML techniques like Support Vector Machines (SVM), Decision Trees (DT), Neural Networks (NN), Bayes Classifiers, and more recently, Ensemble Learners to model and predict activity-travel behaviour. A large number of publications over the years and an upward trend in the number of published articles over time indicate that Machine Learning is a promising tool for activity-travel behaviour analysis and prediction. This article, first of its kind in the literature, reviews these studies and explores the trends in activity-travel behaviour research that apply ML techniques. The review finds that mode choice decisions have received wide attention in the literature on ML applications. It was observed that most of the studies identify the lack of interpretability as a serious shortcoming in ML techniques. However, very few studies have attempted to improve the interpretability of the models. Further, some studies report the importance of feature engineering in ML-based studies, but very few studies adopt feature engineering before model development. Spatiotemporal transferability of models is another issue that has received minimal attention in the literature. In the end, the paper discusses possible directions for future research in the area of activity-travel behaviour modelling using ML techniques.

Landslide disaster risk reduction necessitates the investigation of different geotechnical causal factors for slope failures. Machine learning (ML) techniques have been proposed to study causal factors across many application areas. However, the development of ensemble ML techniques for identifying the geotechnical causal factors for slope failures and their subsequent prediction has lacked in literature. The primary goal of this research is to develop and evaluate novel feature selection methods for identifying causal factors for slope failures and assess the potential of ensemble and individual ML techniques for slope failure prediction. Twenty-one geotechnical causal factors were obtained from 60 sites (both landslide and non-landslide) spread across a landslide-prone area in Mandi, India. Relevant causal factors were evaluated by developing a novel ensemble feature selection method that involved an average of different individual feature selection methods like correlation, information-gain, gain-ratio, OneR, and F-ratio. Furthermore, different ensemble ML techniques (Random Forest (RF), AdaBoost (AB), Bagging, Stacking, and Voting) and individual ML techniques (Bayesian network (BN), decision tree (DT), multilayer perceptron (MLP), and support vector machine (SVM)) were calibrated to 70% of the locations and tested on 30% of the sites. The ensemble feature selection method yielded six major contributing parameters to slope failures: relative compaction, porosity, saturated permeability, slope angle, angle of the internal friction, and in-situ moisture content. Furthermore, the ensemble RF and AB techniques performed the best compared to other ensemble and individual ML techniques on test data. The present study discusses the implications of different causal factors for slope failure prediction.

In the developing world, cancer death is one of the major problems for humankind. Even though there are many ways to prevent it before happening, some cancer types still do not have any treatment. One of the most common cancer types is breast cancer, and early diagnosis is the most important thing in its treatment. Accurate diagnosis is one of the most important processes in breast cancer treatment. In the literature, there are many studies about predicting the type of breast tumors. In this research paper, data about breast cancer tumors from Dr. William H. Walberg of the University of Wisconsin Hospital were used for making predictions on breast tumor types. Data visualization and machine learning techniques including logistic regression, k-nearest neighbors, support vector machine, naïve Bayes, decision tree, random forest, and rotation forest were applied to this dataset. R, Minitab, and Python were chosen to be applied to these machine learning techniques and visualization. The paper aimed to make a comparative analysis using data visualization and machine learning applications for breast cancer detection and diagnosis. Diagnostic performances of applications were comparable for detecting breast cancers. Data visualization and machine learning techniques can provide significant benefits and impact cancer detection in the decision-making process. In this paper, different machine learning and data mining techniques for the detection of breast cancer were proposed. Results obtained with the logistic regression model with all features included showed the highest classification accuracy (98.1%), and the proposed approach revealed the enhancement in accuracy performances. These results indicated the potential to open new opportunities in the detection of breast cancer.

Orthopedic patients have been increasing in hospital because of road traffic accidents, advanced age, a lack of exercise, inadequate nutrition, and other factors. The suggested article uses Machine Learning (ML) techniques to examine the patient reports. The ability to mimic the human actions is called ML. It is a subclass of AI that solves a number of healthcare-related issues. Here ML algorithms are used for health-related data. It solves a number of healthcare-related issues. ML is the process of a machine imitating intelligent human activities. It belongs to the Artificial Intelligence (AI) subclass. ML algorithms are used for medical data such as Logistic Regression, Support vector machine, K-Nearest Neighbor, Random Forest, Decision Tree, Artificial Neural Network to predict orthopedic illnesses such as Normal, Hernia and Spondylolisthesis orthopedic. ML techniques have increased the speed and accuracy for diagnosis. The most serious and urgent cases require rapid care. It improves patient care by lowering human error and stress on medical staff. Our primary objective is to improve machine performance and decrease incorrect categorization.

Prediction of fracture toughness of concrete using the machine learning approach

One of the most serious diseases affecting people is heart disease, which also contributes significantly to the increasing death rate. Recently, Machine Learning (ML) methods have been utilized to detect and identify several types of heart disease, these methods can assist in enhancing the accuracy of heart disease classification. In this research, total ten machine learning (ML) models that’s are used, the first ML technique is Random Forest (rf) second ML technique is Decision Tree (dt), third is Support Vector Machine (svm) fourth ML techniques is K-Nearest Neighbors (knn), fifth ML technique is Logistics Regression (lr) and respectively Extra Trees Classifier (et), Gradient Boosting Classifier (gbc), Naive Bayes (nb), Ridge Classifier (ridge) and tenth ML technique is Linear Discriminant Analysis (ldr) are implemented. In addition, Recursive Feature Elimination (RFE) feature assortment method is used to find the significant features and enhance the efficiency of detecting heart disease using two text-based datasets. Finally, a detailed comparison will be implemented to presented that after smearing the feature selection method to recognize the most efficient model for detecting heart disease and then specify its severity. Some parameters, including accurateness, exactness, recollection, and F1-score, are offered to comparation the efficiency of each classifier. The actual outcomes of this research that improved the classification accurateness.

A machine learning approach for classifying healthy and infarcted patients using heart rate variabilities derived vector magnitude

Impact of pixel-based machine-learning techniques on automated frameworks for delineation of gross tumor volume regions for stereotactic body radiation therapy

This paper reports on the employment of the machine learning (ML) techniques, namely support vector machine (SVM), artificial neural networks (ANN), and random forest (RF), for predicting the tensile behavior of friction stir processed (FSP) dissimilar aluminium alloys joints (6083-T651 and 8011-H14). The dissimilar aluminium joints are fabricated using the friction stir welding (FSW) process. After that, the friction-stir welded joints are subjected to the FSP procedure at different combinations of process parameters. The rotational speed, traverse speed, and tilt angle are used as the input parameters, while tensile strength and grain size are used as the output parameters. In addition, three performance characteristics (i.e., coefficient of correlation (CC), mean absolute error (MAE), and root mean square error (RMSE)) are used to check the adequacy of the developed model of ML techniques. It is observed that support vector machine_radial basis function kernel is the most accurate modeling technique for predicting the tensile behavior of processes samples. Furthermore, the optical microscope is also utilized to check the grain size of the nugget zone (NZ) of the weld bead for FSP. It is found that the minimum grain size (i.e., 5.06 µm) is obtained for the FSP sample and this grain size corresponded to the high ultimate tensile strength (UTS). Moreover, the fractographic analysis showed the ductile behavior of FSW and FSP samples.