Machine Learning
A. Mohamadi; M. Habibi; F. Parandin
Abstract
Background and Objectives: Metastatic castration-sensitive prostate cancer (mCSPC) represents a critical juncture in the management of prostate cancer, where the accurate prediction of the onset of castration resistance is paramount for guiding treatment decisions.Methods: In this study, we underscore ...
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Background and Objectives: Metastatic castration-sensitive prostate cancer (mCSPC) represents a critical juncture in the management of prostate cancer, where the accurate prediction of the onset of castration resistance is paramount for guiding treatment decisions.Methods: In this study, we underscore the power and efficiency of auto-ML models, specifically the Random Forest Classifier, for their low-code, user-friendly nature, making them a practical choice for complex tasks, to develop a predictive model for the occurrence of castration resistance events (CRE (. Utilizing a comprehensive dataset from MSK (Clin Cancer Res 2020), comprising clinical, genetic, and molecular features, we conducted a comprehensive analysis to discern patterns and correlations indicative of castration resistance. A random forest classifier was employed to harness the dataset's intrinsic interactions and construct a robust predictive model. Results: We used over 18 algorithms to find the best model, and our results showed a significant achievement, with the developed model demonstrating an impressive accuracy of 75% in predicting castration resistance events. Furthermore, the analysis highlights the importance of specific features such as 'Fraction Genome Altered ‘and the role of prostate specific antigen (PSA) in castration resistance prediction.Conclusion: Corroborating these findings, recent studies emphasize the correlation between high 'Fraction Genome Altered' and resistance and the predictive power of elevated PSA levels in castration resistance. This highlights the power of machine learning in improving outcome predictions vital for prostate cancer treatment. This study deepens our insights into metastatic castration-sensitive prostate cancer and provides a practical tool for clinicians to shape treatment strategies and potentially enhance patient results.
Classification
K. Kiaei; H. Omranpour
Abstract
Background and Objectives: Time series classification (TSC) means classifying the data over time and based on their behavior. TSC is one of the main machine learning tasks related to time series. Because the classification accuracy is of particular importance, we have decided to increase it in this research.Methods: ...
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Background and Objectives: Time series classification (TSC) means classifying the data over time and based on their behavior. TSC is one of the main machine learning tasks related to time series. Because the classification accuracy is of particular importance, we have decided to increase it in this research.Methods: In this paper, we proposed a simple method for TSC problems to achieve higher classification accuracy than other existing methods. Fast Fourier transform is a method that uses in raw time series data preprocess. In this study, we apply the fast Fourier transform (FFT) over the raw datasets. Then we use the polar form of a complex number to create a histogram. The proposed method consists of three steps: preprocessing using FFT, feature extraction by histogram computation, and decision making using a random forest classifier.Results: The presented method was tested on 12 datasets of the UCR time series classification archive from different domains. Evaluation of our method was performed using k-fold cross-validation and classification accuracy. The experimental results state that our model has been achieved classification accuracy higher or comparable than related methods. Computational complexity has also been significantly reduced.Conclusion: In the latest years, the TSC problems have been increased. In this work, we proposed a simple method with extracted features from fast Fourier transforms that is efficient to gain more high accuracy.
