Explainable AI to Predict Male Fertility Using Extreme Gradient Boosting Algorithm with SMOTE

GhoshRoy, Debasmita; Alvi, P. A.; Santosh, K. C.

doi:10.3390/electronics12010015

Cited by 15 publications

(5 citation statements)

References 40 publications

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“…Using the ESLSMOTE model, researchers found an over 97.2% ROC rating for predicting seminal quality [41]. Ghsohroy et al used the extreme gradient boost (XGB) AI tool with ESLSMOTE modeling to obtain a 93.22% mean accuracy and a 0.98 AUC [42]. Models such as the feed-forward neural network (FFNN) have been compared with common machine learning algorithms such as MLPs and SVMs and have a high predictive accuracy [43].…”

Section: Using Ann and DL To Predict Seminal Qualitymentioning

confidence: 99%

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

Venishetty,

Alkassis,

Raheem

2024

Uro

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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.

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Section: Using Ann and DL To Predict Seminal Qualitymentioning

confidence: 99%

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

Venishetty,

Alkassis,

Raheem

2024

Uro

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“…The Synthetic Minority Over-sampling Technique (SMOTE) [51] used in this study to address data imbalance creates synthetic representations of the minority class through interpolation. The described mode of operation includes selecting two samples from adjacent minority classes and generating a new sample between both through the incorporation of a proportion of features from each.…”

Section: Data Augmentationmentioning

confidence: 99%

“…Certain investigations applied PCA feature extraction method for model optimization [50] by reducing data dimensionality and computational burden, as well as expediting the classification process. SMOTE data balancing has been used to improve classification model performance on unbalanced datasets [51] Additionally, parameter-tuning was found to be capable of optimizing algorithm performance [52]- [56]. The superior abilities of these methods lead to the proposal of an SVM algorithm model with aspects including PCA-based feature extraction, SMOTE for data balancing, and GridSearchCV for parameter tuning, thereby enhancing accuracy in PD detection.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Support Vector Machine Performance for Parkinson's Disease Diagnosis Using GridSearchCV and PCA-Based Feature Extraction

Jumanto,

Rofik,

Sugiharti

et al. 2024

J. Inf. Syst. Eng. Bus. Intell.

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Background: Parkinson's disease (PD) is a critical neurodegenerative disorder affecting the central nervous system and often causing impaired movement and cognitive function in patients. In addition, its diagnosis in the early stages requires a complex and time-consuming process because all existing tests such as electroencephalography or blood examinations lack effectiveness and accuracy. Several studies explored PD prediction using sound, with a specific focus on the development of classification models to enhance accuracy. The majority of these neglected crucial aspects including feature extraction and proper parameter tuning, leading to low accuracy. Objective: This study aims to optimize performance of voice-based PD prediction through feature extraction, with the goal of reducing data dimensions and improving model computational efficiency. Additionally, appropriate parameters will be selected for enhancement of the ability of the model to identify both PD cases and healthy individuals. Methods: The proposed new model applied an OpenML dataset comprising voice recordings from 31 individuals, namely 23 PD patients and 8 healthy participants. The experimental process included the initial use of the SVM algorithm, followed by implementing PCA for feature extraction to enhance machine learning accuracy. Subsequently, data balancing with SMOTE was conducted, and GridSearchCV was used to identify the best parameter combination based on the predicted model characteristics. Result: Evaluation of the proposed model showed an impressive accuracy of 97.44%, sensitivity of 100%, and specificity of 85.71%. This excellent result was achieved with a limited dataset and a 10-fold cross-validation tuning, rendering the model sensitive to the training data. Conclusion: This study successfully enhanced the prediction model accuracy through the SVM+PCA+GridSearchCV+CV method. However, future investigations should consider an appropriate number of folds for a small dataset, explore alternative cross-validation methods, and expand the dataset to enhance model generalizability. Keywords: GridSearchCV, Parkinson Disaese, SVM, PCA, SMOTE, Voice/Speech

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“…To overcome this problem, SMOTE was proposed and implemented [49]. It works by adding new samples to minority classes during the training phase only.…”

Section: Balance Of Datasetmentioning

confidence: 99%

Automatic and Early Detection of Parkinson’s Disease by Analyzing Acoustic Signals Using Classification Algorithms Based on Recursive Feature Elimination Method

et al. 2023

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Parkinson’s disease (PD) is a neurodegenerative condition generated by the dysfunction of brain cells and their 60–80% inability to produce dopamine, an organic chemical responsible for controlling a person’s movement. This condition causes PD symptoms to appear. Diagnosis involves many physical and psychological tests and specialist examinations of the patient’s nervous system, which causes several issues. The methodology method of early diagnosis of PD is based on analysing voice disorders. This method extracts a set of features from a recording of the person’s voice. Then machine-learning (ML) methods are used to analyse and diagnose the recorded voice to distinguish Parkinson’s cases from healthy ones. This paper proposes novel techniques to optimize the techniques for early diagnosis of PD by evaluating selected features and hyperparameter tuning of ML algorithms for diagnosing PD based on voice disorders. The dataset was balanced by the synthetic minority oversampling technique (SMOTE) and features were arranged according to their contribution to the target characteristic by the recursive feature elimination (RFE) algorithm. We applied two algorithms, t-distributed stochastic neighbour embedding (t-SNE) and principal component analysis (PCA), to reduce the dimensions of the dataset. Both t-SNE and PCA finally fed the resulting features into the classifiers support-vector machine (SVM), K-nearest neighbours (KNN), decision tree (DT), random forest (RF), and multilayer perception (MLP). Experimental results proved that the proposed techniques were superior to existing studies in which RF with the t-SNE algorithm yielded an accuracy of 97%, precision of 96.50%, recall of 94%, and F1-score of 95%. In addition, MLP with the PCA algorithm yielded an accuracy of 98%, precision of 97.66%, recall of 96%, and F1-score of 96.66%.

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Explainable AI to Predict Male Fertility Using Extreme Gradient Boosting Algorithm with SMOTE

Cited by 15 publications

References 40 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

Optimizing Support Vector Machine Performance for Parkinson's Disease Diagnosis Using GridSearchCV and PCA-Based Feature Extraction

Automatic and Early Detection of Parkinson’s Disease by Analyzing Acoustic Signals Using Classification Algorithms Based on Recursive Feature Elimination Method

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