Semi-Supervised Learning with GANs for Melanoma Detection

Agarwal, Nidhi; Singh, Vikas; Singh, Pratham

doi:10.1109/iciccs53718.2022.9787990

Cited by 16 publications

(9 citation statements)

References 17 publications

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“…also explored it by demonstrating the versatility of machine learning models for various classification tasks [14]. applied medical image analysis, the domain relevant to PD diagnosis [15]. focused on predicting fire outbreaks, showcasing the utility of historical databases and predictive modelling, which could be adapted to PD risk assessment in the future [16].…”

Section: Literature Surveymentioning

confidence: 99%

Predictive Modelling for Parkinson's Disease Diagnosis using Biomedical Voice Measurements

Dahiya,

Deepakshi

et al. 2024

EAI Endorsed Trans Perv Health Tech

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INTRODUCTION: Parkinson's Disease (PD), a progressively debilitating neurological disorder impacting a substantial global population, stands as a significant challenge in modern healthcare. The gradual onset of motor and non-motor symptoms underscores the criticality of early detection for optimal treatment outcomes. In response to this urgency, novel avenues for early diagnosis are being explored, where the amalgamation of biomedical voice analysis and advanced machine learning techniques holds immense promise. Individuals afflicted by PD experience a nuanced deterioration of bodily functions, necessitating interventions that are most effective when initiated at an early stage. The potential of biomedical voice measurements to encode subtle health indicators presents an enticing opportunity. The human voice, an intricate interplay of frequencies and patterns, might offer insights into the underlying health condition. OBJECTIVES: This research embarks on a comprehensive journey to delve into the intricate connections between voice attributes and the presence of PD, with the aim of expediting its detection and treatment. METHODS: At the heart of this exploration is the Support Vector Machine (SVM) model, a versatile machine learning tool [1-2]. Functioning as a virtual detective, the SVM model learns from historical data to decipher the intricate patterns that differentiate healthy individuals from those with PD [3-4]. RESULTS: Through the power of pattern recognition, the SVM becomes a predictive instrument, a potential catalyst in unravelling the latent manifestations of PD using the unique patterns harbored within the human voice. Embedded within this research are the practical demonstrations showcased through code snippets [5-7]. By synergizing the intricate voice measurements with the SVM model, we envision the emergence of a diagnostic paradigm where early PD detection becomes both accessible and efficient. This study not only epitomizes the synergy of voice and machine interactions but also attests to the transformative potential of technology within the domain of healthcare. . CONCLUSION: Ultimately, this research strives to harness the intricate layers of voice data, as exemplified through the provided model code [8-11], to contribute to the evolution of an advanced tool for PD prediction. By amalgamating the principles of machine learning and biomedical analysis, we aspire to expedite early PD diagnosis, thereby catalyzing more efficacious treatment strategies. In traversing this multidimensional exploration, we aspire to pave the path toward a future where technology plays an instrumental role in enhancing healthcare outcomes for individuals navigating the challenges of PD, ultimately advancing the pursuit of early diagnosis and intervention.

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Section: Literature Surveymentioning

confidence: 99%

Predictive Modelling for Parkinson's Disease Diagnosis using Biomedical Voice Measurements

Dahiya,

Deepakshi

et al. 2024

EAI Endorsed Trans Perv Health Tech

Self Cite

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“…• Receiver Operating Characteristic (ROC) Analysis: Perform an in-depth analysis of the ROC curves generated by each classifier [25]. The ROC curve provides insights into the trade-off between sensitivity and specificity across various decision thresholds and aids in selecting an appropriate threshold for practical application [12,28,29].…”

Section: Aims Of the Studymentioning

confidence: 99%

Predictive Modelling for Heart Disease Diagnosis: A Comparative Study of Classifiers

Agarwal,

Deepakshi,

Harikiran

et al. 2024

EAI Endorsed Trans Perv Health Tech

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INTRODUCTION: Cardiovascular diseases, including heart disease, remain a significant cause of morbidity and mortality worldwide. Timely and accurate diagnosis of heart disease is crucial for effective intervention and patient care. With the emergence of machine learning techniques, there is a growing interest in leveraging these methods to enhance diagnostic accuracy and predict disease outcomes. OBJECTIVES: This study evaluates the performance of three machine learning classifiers—Naive Bayes, Logistic Regression, and k-Nearest Neighbors in predicting heart disease based on patient attributes. METHODS: In this study, we explore the application of three prominent machine learning classifiers—Naive Bayes, Logistic Regression, and k-Nearest Neighbors (kNN)—to predict the presence of heart disease based on a set of patient attributes. RESULTS: Using a dataset of 303 patient records with 14 attributes, including age, sex, and cholesterol levels, the data is pre-processed, scaled, and split into training and test sets. Each classifier is trained on the training set and evaluated on the test set. Results reveal that Naive Bayes and k-Nearest Neighbors classifiers outperform Logistic Regression in terms of accuracy, precision, recall, and area under the ROC curve (AUC). CONCLUSION: This study underscores the promising role of machine learning in medical diagnosis, showcasing the potential of Naive Bayes and k-Nearest Neighbors classifiers in improving heart disease prediction accuracy. Future work could explore advanced classifiers and feature selection techniques to enhance predictive accuracy and generalize findings to larger datasets.

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“…Synthetic Minority Oversampling Technology is referred to as SMOTE. This module allows us to create artificial samples for the minority class [19][20]. It also avoids the issue of overfitting that arises from random oversampling in this manner.…”

Section: Plotting Datamentioning

confidence: 99%

A Novel Methodology for Hunting Exoplanets in Space Using Machine Learning

Singh,

Agarwal,

Yadav

2024

EAI Endorsed Trans IoT

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INTRODUCTION: Exoplanet exploration outside of our solar system has recently attracted attention among astronomers worldwide. The accuracy of the currently used detection techniques, such as the transit and radial velocity approaches is constrained. Researchers have suggested utilizing machine learning techniques to create a prediction model to increase the identification of exoplanets beyond our milky way galaxy. OBJECTIVES: The novel method proposed in this research paper builds a prediction model using a dataset of known exoplanets and their characteristics, such as size, distance from the parent star, and orbital period. The model is then trained using this data based on machine learning methods that Support Vector Machines and Random Forests. METHODS: A different dataset of recognized exoplanets is used to assess the model’s accuracy, and the findings are compared with in comparison to accuracy rates of the transit and radial velocity approaches. RESULTS: The prediction model created in this work successfully predicts the presence of exoplanets in the test data-set with an accuracy rate of over 90 percent. CONCLUSION: This discovery shows the promise and confidence of machine learning techniques for exoplanet detection.

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Semi-Supervised Learning with GANs for Melanoma Detection

Cited by 16 publications

References 17 publications

Predictive Modelling for Parkinson's Disease Diagnosis using Biomedical Voice Measurements

Predictive Modelling for Parkinson's Disease Diagnosis using Biomedical Voice Measurements

Predictive Modelling for Heart Disease Diagnosis: A Comparative Study of Classifiers

A Novel Methodology for Hunting Exoplanets in Space Using Machine Learning

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