Prediction of secondary testosterone deficiency using machine learning: A comparative analysis of ensemble and base classifiers, probability calibration, and sampling strategies in a slightly imbalanced dataset

Novaes, Monique; Carvalho, Osmar Luiz Ferreira de; Ferreira, Pedro Henrique Guimarães; Tiraboschi, Taciana; Silva, Caroline Santos; Zambrano, Jean C.; Gomes, Cristiano Mendes; Miranda, Eduardo Foschini; Júnior, Osmar Abı́lio de Carvalho; Júnior, José de Bessa

doi:10.1016/j.imu.2021.100538

Cited by 6 publications

(8 citation statements)

References 110 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Studies have found that low-testosterone males have lower semen volume and sperm cell counts in comparison with patients with normal levels of testosterone [61]. Moreover, many patients are diagnosed with testosterone deficiency syndrome (TDS) and can experience primary and secondary hypogonadism [62]. Studies have used ensemble-based classifiers within the domain of machine learning to predict TDS at an earlier stage [62].…”

Section: Anatomical Variations and Ai: Implications For Male Infertil...mentioning

confidence: 99%

“…Moreover, many patients are diagnosed with testosterone deficiency syndrome (TDS) and can experience primary and secondary hypogonadism [62]. Studies have used ensemble-based classifiers within the domain of machine learning to predict TDS at an earlier stage [62]. Ensemble classifiers, particularly the Weighted Average Ensemble Classifier (wAvg), have outperformed single classifiers, with XGBoost being the best among them [62].…”

Section: Anatomical Variations and Ai: Implications For Male Infertil...mentioning

confidence: 99%

“…Studies have used ensemble-based classifiers within the domain of machine learning to predict TDS at an earlier stage [62]. Ensemble classifiers, particularly the Weighted Average Ensemble Classifier (wAvg), have outperformed single classifiers, with XGBoost being the best among them [62]. Moreover, calibration techniques improve predictions and feature importance analysis highlighting the significance of abdominal circumference, triglycerides, diabetes, and high-density lipoprotein in TDS prediction [62].…”

Section: Anatomical Variations and Ai: Implications For Male Infertil...mentioning

confidence: 99%

See 2 more Smart Citations

The Role of Artificial Intelligence in Male Infertility: Evaluation and Treatment: A Narrative Review

Venishetty,

Alkassis,

Raheem

2024

Uro

View full text Add to dashboard Cite

Male infertility has affected an increasingly large population over the past few decades, affecting over 186 million people globally. The advent of assisted reproductive technologies (ARTs) and artificial intelligence (AI) has changed the landscape of diagnosis and treatment of male infertility. Through an extensive literature review encompassing the PubMed, Google Scholar, and Scopus databases, various AI techniques such as machine learning (ML), artificial neural networks (ANNs), deep learning (DL), and natural language processing (NLP) were examined in the context of evaluating seminal quality, predicting fertility potential, and improving semen analysis. Research indicates that AI models can accurately estimate the quality of semen, diagnose problems with sperm, and provide guidance on reproductive health decisions. In addition, developments in smartphone-based semen analyzers and computer-assisted semen analysis (CASA) are indicative of initiatives to improve the price, portability, and accuracy of results. Future directions point to possible uses for AI in ultrasonography assessment, microsurgical testicular sperm extraction (microTESE), and home-based semen analysis. Overall, AI holds significant promise in revolutionizing the diagnosis and treatment of male infertility, offering standardized, objective, and efficient approaches to addressing this global health challenge.

show abstract

Section: Anatomical Variations and Ai: Implications For Male Infertil...mentioning

confidence: 99%

Section: Anatomical Variations and Ai: Implications For Male Infertil...mentioning

confidence: 99%

Section: Anatomical Variations and Ai: Implications For Male Infertil...mentioning

confidence: 99%

See 1 more Smart Citation

The Role of Artificial Intelligence in Male Infertility: Evaluation and Treatment: A Narrative Review

Venishetty,

Alkassis,

Raheem

2024

Uro

View full text Add to dashboard Cite

show abstract

“…After each baseline model is stacked, the prediction result is input to the Meta Classifier, and the final classification prediction is performed. (3) Model evaluation: using the within-project Stratified K-Fold (K = 10) [24] cross-validation and cross-project verification experimental methods, two comprehensive evaluation indicators: Area Under Curve (AUC), and F1-score on the generalization performance of the model authenticating.…”

Section: Nested-stacking Frameworkmentioning

confidence: 99%

Software defect prediction based on nested-stacking and heterogeneous feature selection

Chen

Wang

Song

2022

Complex Intell. Syst.

View full text Add to dashboard Cite

Software testing guarantees the delivery of high-quality software products, and software defect prediction (SDP) has become an important part of software testing. Software defect prediction is divided into traditional software defect prediction and just-in-time software defect prediction (JIT-SDP). However, most of the existing software defect prediction frameworks are relatively simplified, which makes it extremely difficult to provide developers with more detailed reference information. To improve the effectiveness of software defect prediction and realize effective software testing resource allocation, this paper proposes a software defect prediction framework based on Nested-Stacking and heterogeneous feature selection. The framework includes three stages: data set preprocessing and feature selection, Nested-Stacking classifier, and model classification performance evaluation. The novel heterogeneous feature selection and nested custom classifiers in the framework can effectively improve the accuracy of software defect prediction. This paper conducts experiments on two software defect data sets (Kamei, PROMISE), and demonstrates the classification performance of the model through two comprehensive evaluation indicators, AUC, and F1-score. The experiment carried out large-scale within-project defect prediction (WPDP) and cross-project defect prediction (CPDP). The results show that the framework proposed in this paper has an excellent classification performance on the two types of software defect data sets, and has been greatly improved compared with the baseline models.

show abstract

“…The recent developments in the miniaturization of inertial sensors equipped with state-of-the-art processing and communication capabilities lay the foundations for the smart health and activity monitoring using machine learning techniques [ 8 , 9 ]. Wearable inertial measurement units (IMUs) use accelerometers and gyroscopes to measure acceleration and angular velocities to offer unobtrusive, reliable, and low-cost measurement of sensory data for physical activity classification.…”

Section: Introductionmentioning

confidence: 99%

Physical Activity Monitoring and Classification Using Machine Learning Techniques

Alsareii

Awais

Alamri

et al. 2022

Life

View full text Add to dashboard Cite

Physical activity plays an important role in controlling obesity and maintaining healthy living. It becomes increasingly important during a pandemic due to restrictions on outdoor activities. Tracking physical activities using miniature wearable sensors and state-of-the-art machine learning techniques can encourage healthy living and control obesity. This work focuses on introducing novel techniques to identify and log physical activities using machine learning techniques and wearable sensors. Physical activities performed in daily life are often unstructured and unplanned, and one activity or set of activities (sitting, standing) might be more frequent than others (walking, stairs up, stairs down). None of the existing activities classification systems have explored the impact of such class imbalance on the performance of machine learning classifiers. Therefore, the main aim of the study is to investigate the impact of class imbalance on the performance of machine learning classifiers and also to observe which classifier or set of classifiers is more sensitive to class imbalance than others. The study utilizes motion sensors’ data of 30 participants, recorded while performing a variety of daily life activities. Different training splits are used to introduce class imbalance which reveals the performance of the selected state-of-the-art algorithms with various degrees of imbalance. The findings suggest that the class imbalance plays a significant role in the performance of the system, and the underrepresentation of physical activity during the training stage significantly impacts the performance of machine learning classifiers.

show abstract

Prediction of secondary testosterone deficiency using machine learning: A comparative analysis of ensemble and base classifiers, probability calibration, and sampling strategies in a slightly imbalanced dataset

Cited by 6 publications

References 110 publications

The Role of Artificial Intelligence in Male Infertility: Evaluation and Treatment: A Narrative Review

The Role of Artificial Intelligence in Male Infertility: Evaluation and Treatment: A Narrative Review

Software defect prediction based on nested-stacking and heterogeneous feature selection

Physical Activity Monitoring and Classification Using Machine Learning Techniques

Contact Info

Product

Resources

About