A Logistic Regression Based Approach for Software Test Management

Recent advances in machine learning techniques have significantly influenced the development of early diabetes detection systems. In this paper, different machine learning (ML) algorithms are employed for diabetes detection based on hand movement. The spatial and temporal information for horizontal and vertical hand movement deviations for both diabetic and non-diabetic subjects are obtained using a data acquisition system. A dataset is constructed based on the obtained hand movement information along with other related attributes such as age, gender, weight and vision health. Binary classification of diabetic and non-diabetic subjects is achieved using several machine learning algorithms. These algorithms include Naïve Bayes (NB), Logistic Regression (LR), Linear Discriminate Analysis (LDA), Classification and Regression Tree (CART), k-Nearest Neighbors (KNN), Support Vector Machine (SVM) and Multi-layer Perceptron (MLP). The considered ML algorithms are evaluated and ranked according to a set of key performance metrics that include accuracy, precision, recall and F-1 score. Algorithm ranking results demonstrate that LR, CART, KNN and MLP algorithms have achieved higher ranking positions as compared to other algorithms. The best ranked ML algorithms are then validated and thoroughly investigated to prove their ability to discriminate between diabetic and non-diabetic subjects. Validation results show that the best-ranked ML algorithms have achieved high classification accuracy, precision, recall and F-1 scores. Therefore, the considered machine learning algorithms can serve as efficient tools for diabetes detection based on the constructed dataset.

In the current times, COVID-19 has taken a handful of people’s lives. So, vaccination is crucial for everyone to avoid the spread of the disease. However, not every vaccine will be perfect or will get success for everyone. In the present work, we have analyzed the data from the Vaccine Adverse Event Reporting System and understood that the vaccines given to the people might or might not work considering certain demographic factors like age, gender, and multiple other variables like the state of living, etc. This variable is considered because it explains the unmentioned variables like their food habits and living conditions. The target group for this work will be the healthcare workers, government bodies & medical research organizations. We analyze the data using machine learning techniques & algorithms and predict the working of COVID-19 vaccines on specific age groups developed by significant vaccine manufacturers, i.e., PFIZER\BIONTECH and MODERNA. Data visualization and analysis interpret the vaccine impact based on the above-said variables. It becomes clear that people belonging to a specific demographic factor can have an option to choose the vaccine accordingly based on the previous history of a particular manufacturer’s vaccine getting succeeded for that demographic factor. The various machine learning algorithms we have used are Logistic Regression, Adaboost, Decision Tree, and Random Forest. We have considered the DIED variable as the target variable as this results in a high life threat. On performance measure, perspective Adaboost is showing appreciable values. The prediction of the type of vaccine to be administered could be derived using this machine learning algorithm. The accuracy we achieved based on the experiment are as follows: Decision Tree Classifier with 97.3%, Logistic Regression with 97.31%, Random Forest with 97.8%, AdaBoost with 98.1%.

The aim of this paper is to review machine learning (ML) algorithms and techniques for hate speech detection in social media (SM). Hate speech problem is normally model as a text classification task. In this study, we examined the basic baseline components of hate speech classification using ML algorithms. There are five basic baseline components – data collection and exploration, feature extraction, dimensionality reduction, classifier selection and training, and model evaluation, were reviewed. There have been improvements in ML algorithms that were employed for hate speech detection over time. New datasets and different performance metrics have been proposed in the literature. To keep the researchers informed regarding these trends in the automatic detection of hate speech, it calls for a comprehensive and an updated state-of-the-art. The contributions of this study are three-fold. First to equip the readers with the necessary information on the critical steps involved in hate speech detection using ML algorithms. Secondly, the weaknesses and strengths of each method is critically evaluated to guide researchers in the algorithm choice dilemma. Lastly, some research gaps and open challenges were identified. The different variants of ML techniques were reviewed which include classical ML, ensemble approach and deep learning methods. Researchers and professionals alike will benefit immensely from this study.

e17570 Background: Ovarian cancer(OC) is the leading cause of death from gynecologic malignancy. Current challenges include lack of diagnostic tools, predictive biomarkers, and identifying appropriate surgical candidates. Machine learning(ML) is an emerging field that can make accurate projections by making inferences on data and may play a crucial role in OC.The objective of the current study was to review the literature on application of ML in OC and report the most commonly used algorithms and their performance in comparison to existing prediction tools and traditional regression models. Methods: This is a systematic review of published literature from January 1985 to March 2021 on the use of ML in OC. An extensive search of electronic library databases was conducted. Four independent reviewers screened the articles initially by title then full text. Quality was assessed using the MINORS criteria. P-values were generated using the Pearson’s Chi-squared(x2) test to compare performance of ML models with traditional statistics. No p-values were reported if only one study was available. Results: Among 4,295 articles screened, 88 studies on ML in OC were included. The mean age of OC patients was 54.7 years(11-90) and the most common stages at diagnosis were:Stage III (39.9%) and IV (34%). Applications of ML were in clinical datasets(33%, n = 29), preoperative diagnostics(30.7%, n = 27), serum biomarkers (21.6%, n = 19), genomics (12.5%, n = 11), and prediction of cytoreductive outcomes (2.3%, n = 2). The most commonly applied algorithms were Support Vector Machine [SVM](28%, n = 33)and Neural Networks[NN] (25.28%). Over the past decades, the number of publications on ML in OC increased three-fold from 20(1994-2010) to 67 (2011–2021). Only 9 (10%) studies compared ML techniques with existing prediction tools, or traditional regression models. Among 29 clinical dataset studies, 4 compared ML with traditional logistic regression(LR). Two studies reported better performance with ML compared to LR but not significant(accuracy: 0.88 vs 0.84, p = 0.15), one study performed comparably(accuracy: 0.1 vs 0.1) while one study performed worse(accuracy: 0.1 vs 0.97). Only one preoperative diagnostic study compared ML techniques with LR. SVM classifiers outperformed LR in classifying ovarian masses as benign or malignant(sensitivity: 0.88 vs. 0.70). One serum biomarker study compared LR with ML algorithms; LR performed better using two biomarkers for predicting OC(accuracy: 0.97 vs. 0.94). Among five studies reporting overall survival outcomes, only one study compared survival ML techniques using NN with LR and showed that NN classifiers outperformed LR in predicting overall survival(AUC: 0.72 vs. 0.62). Conclusions: This is the first systematic review exploring the literature on ML algorithms in OC. Most ML models outperformed traditional models. However, larger datasets would be required to validate findings.

Introduction: In the skeletal system, the most dimorphic bones employed for postmortem gender prediction include the bones in the pelvic skeleton. Bone measurements are usually conducted with cadaver bones. Computed tomography (CT) is an increasingly popular method due to its ease of use, reconstruction opportunities, and lower impact of age bias and provides a modern data source. Even when parameters obtained with different or same bones are missing, machine learning (ML) algorithms allow the use of statistical methods to predict gender. This study was carried out in order to obtain high accuracy in estimating gender with the pelvis skeleton by integrating ML algorithms, which are used extensively in the field of engineering, in the field of health. Material and Methods: In the present study, pelvic CT images of 300 healthy individuals (150 females, 150 males) between the ages of 25 and 50 (the mean female age = 40, the mean male age = 37) were transformed into orthogonal images, and landmarks were placed on promontory, iliac crest, sacroiliac joint, anterior superior iliac spine, anterior inferior iliac spine, terminal line, obturator foramen, greater trochanter, lesser trochanter, femoral head, femoral neck, body of femur, ischial tuberosity, acetabulum, and pubic symphysis, and coordinates of these regions were obtained. Four groups were formed based on various angle and length combinations obtained from these coordinates. These four groups were analyzed with ML algorithms such as Logistic Regression, Linear Discriminant Analysis (LDA), Random Forest, Extra Trees Classifier, and ADA Boost Classifier. Results: In the analysis, it was determined that the highest accuracy was 0.96 (sensitivity 0.95, specificity 0.97, Matthew's Correlation Coefficient 0.93) with LDA. Discussion and Conclusion: The use of length and angle measurements obtained from the pelvis showed that the LDA model was effective in estimating gender.

Across many fields of social science, machine learning (ML) algorithms are rapidly advancing research as tools to support traditional hypothesis testing research (e.g., through data reduction and automation of data coding or for improving matching on observable features of a phenomenon or constructing instrumental variables). In this paper, we argue that researchers are yet to recognize the value of ML techniques for theory building from data. This may be in part because of scholars’ inherent distaste for predictions without explanations that ML algorithms are known to produce. However, precisely because of this property, we argue that ML techniques can be very useful in theory construction during a key step of inductive theorizing—pattern detection. ML can facilitate algorithm supported induction, yielding conclusions about patterns in data that are likely to be robustly replicable by other analysts and in other samples from the same population. These patterns can then be used as inputs to abductive reasoning for building or developing theories that explain them. We propose that algorithm-supported induction is valuable for researchers interested in using quantitative data to both develop and test theories in a transparent and reproducible manner, and we illustrate our arguments using simulations.

Recent developments in machine learning (ML) techniques present a promising new analysis method for high-speed imaging in astroparticle physics experiments, for example with imaging atmospheric Cherenkov telescopes (IACTs). In particular, the use of timing information with new machine learning techniques provides a novel method for event classification. Previous work in this field has utilised images of the integrated charge from IACT camera photomultipliers, but the majority of current and upcoming IACT cameras have the capacity to read out the entire photosensor waveform following a trigger. As the arrival times of Cherenkov photons from extensive air showers (EAS) at the camera plane are dependent upon the altitude of their emission, these waveforms contain information useful for IACT event classification. In this work, we investigate the potential for using these waveforms with ML techniques, and find that a highly effective means of utilising their information is to create a set of seven additional two dimensional histograms of waveform parameters to be fed into the machine learning algorithm along with the integrated charge image. This appears to be superior to using only these new ML techniques with the waveform integrated charge alone. We also examine these timing-based ML techniques in the context of other experiments.

Slope stability prediction using integrated metaheuristic and machine learning approaches: A comparative study

Sentiment Analysis is a widely used text classification technique. It breaks down any given text or comments and classify the text either as positive or negative based on the views conveyed in it. Previous works done on sentiment classification used either lexicon based approach or machine learning techniques. Likewise, the major drawback of the existing systems was the focus on only binary classification of review such as positive or negative. Ignorance of the neutral review will result in misinterpretation of a customer's opinion about a product or movie, which will degrade the business or trend. In case of using only lexicon based approach, the system highly depends on the selection of lexicon resource and dictionary. In case of system built only using machine learning approach, the performance of the system depends on the algorithms chosen. This work presents a hybrid model to resolve the neutral class too. The proposed work combines a lexical approach (SentiWordNet) with the machine learning algorithms such as Support Vector Machine, Decision Tree, Logistic Regression and Naive Bayes for sentiment analysis to resolve the neutral opinions beyond the binary categorization of the customer's review. We have also compared the performance of these four machine learning algorithms along with the lexicon approach. The results proved that Support Vector Machine and Logistic Regression algorithms outperform the other two algorithms with an accuracy of about 80% which is on average differs by 6% to 10% when compared to other algorithms.

This research study intends to identify hypertension by using machine learning techniques. When blood vessel pressure rises, it results in hypertension (high blood pressure), this condition can cause heart disease and possibly renal failure. Therefore, the early diagnosis can eliminate high-risk and be treated in the early stages, which is made feasible by Machine Learning (ML) algorithms. In this work, machine learning algorithms such as random forests, decision trees, multinomial naive bayes, and logistic regression are applied. The model performance is then determined by their accuracy, F1 score, recall, etc. It has been found through a comparison of algorithms that the accuracy of both Logistic Regression (LR) and Decision Tree (DT) are higher.

One of the leading causes of death in people is a brain tumor. A life can be saved by brain tumor early identification. Finding a brain tumor is quite difficult. Brain anomalies can be found using ML (Machine Learning) methods. Nine ML techniques were utilized in the experimental setting on a dataset of more than 3700 photos. These strategies include a number of well-known techniques, such as logistic regression and artificial neural networks (ANN). To understand how the distribution of the dataset and the train-test split effect accuracy and false negatives, it was important to assess how accurate different algorithms performed as well as to dramatically reduce false negatives. Using ANN and logistic regression on 2000 images with extracted features, the results were 99.5 percent accurate with a 0.0001 percent false negative rate. If the data sample used is unsuitable, accuracy is also negatively impacted by over 80%, in addition to false positives. The study showed a notable decrease in false positives with a 67:38 data distribution. This research will help in the creation of generalized models whose performance can be maintained irrespective of the underlying tumor image data.

Rainfall prediction is a beneficiary one, but it is a challenging task. Machine learning techniques can use computational methods and predict rainfall by retrieving and integrating the hidden knowledge from the linear and non-linear patterns of past weather data. This paper investigates the effectiveness of two machine learning algorithms - Logistic Regression (LR) and Random Forest (RF) - for rainfall prediction using historical datasets. This study would assist researchers in analysing the most recent work on rainfall prediction with an emphasis on machine learning techniques and providing a reference for possible guidance and comparisons. Anaconda framework is used, and the coding language used is Python, which is portable and dynamic. NumPy, matplotlib, seaborn, and pandas are the libraries used for the implementation. The main objective of this paper is to identify the relevant atmospheric features that cause rainfall and predict the intensity of daily rainfall using machine learning techniques. This project focuses on harnessing the power of basic machine learning algorithms to unravel insights from a historical rainfall dataset. Insights are interpreted through feature importance analysis and visualization of decision tree structures. In essence, this project showcases the efficiency of basic machine learning algorithms in uncovering hidden information within a rainfall dataset.

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.

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.

In order to enhance treatment outcomes and reduce mortality, brain stroke, which is a severe medical condition caused by an interruption of blood supply to the brain, requires a prompt and accurate diagnosis. Machine learning algorithms have emerged as very efficient tools in medical diagnosis over the past few years, particularly in identification and prognosis of brain strokes. Of all such algorithms, the Support Vector Machine (SVM) algorithm has performed incredibly well, with a highest accuracy rate of 94.58%. The use of SVM to diagnose brain stroke is explored in this paper, focusing on its performance in the ability to distinguish, based on clinical and imaging information, ischemic from hemorrhagic strokes. The SVM model has been extensively trained and tested using a huge dataset with various risk factors and diagnostic characteristics and has shown its remarkable ability to classify stroke type with incredible accuracy. The findings emphasize the potential of SVM in aiding doctors in making timely and accurate diagnoses of strokes, which will in turn result in improved patient care and outcomes. Future research ought to focus on integrating SVM with other machine learning techniques so as to enhance diagnostic accuracy further and evaluate its viability in real-time clinical settings.