![]() ![]() In which, we are having less priority features to be removed. In this type of prediction type of model, we can have some issues related to overfitting model. By the help of machine learning methodologies, we can analyze the cancerous tumors of a human body with high accuracy. Machine Learning is one of the trending domains in the computer science discipline and also machine learning algorithms are so effective for analyzing the biological datasets. For the early stage diagnosis, we are can apply machine learning techniques. As we know the cancer is a non-curable disease if we diagnose at the last stages and it is also not an easy task to diagnose the cancer at early stages also. In cancer disease, there are four stages in which first two stages are called as early stage and the last two stages are known as last stages. Results show an appropriate performance in predicting other illnesses as well. ![]() Moreover, to evaluate the performance of the proposed framework, it has been implemented on three other medical datasets. Using this framework, breast cancer recovery and therapy will be more successful. Furthermore, we recommend that this proposed framework be used to diagnose breast cancer in its early stages as it works effectively. Based on the findings, we noticed that the suggested framework’s performance works perfectly due to the selection of more appropriate features by the Extra Trees algorithm. The proposed method resulted in a 97% F1-score and 98% precision tested on Wisconsin Diagnosed Diagnostic Breast Cancer (WDBC) dataset. Finally, three approaches, including Boosting, Bagging, and Voting, were combined with equal weights together through the Stacking approach. In this framework, to extract the most relevant features, the Extra Trees classifier is used to integrate the attributes obtained from Variance Inflation Factor, Pearson’s Correlation, and Information Gain to detect the tumors’ hidden patterns. This research proposes a new ensemble-based framework named Meta-Health Stack to predict breast cancer more efficiently. Machine learning algorithms are beneficial for finding a significant relationship between various features and malignant tumors. Filtering those to obtain an accurate diagnosis is time-consuming and challenging. Different tumor features are available in various datasets for breast cancer detection. Recent statistics indicate that breast cancer is the most commonly diagnosed cancer worldwide. Our findings provided more insights into schizophrenia neuropathology.ĭata analytics and machine learning have grown in importance to efficiently manage large amounts of healthcare data. In individuals with schizophrenia, different brain regions demonstrate dynamic deviations of brain aging trajectories. The accelerated aging in 22 GM regions and 10 white matter tracts in schizophrenia potentially exacerbates with disease progression. However, no accelerated brain aging was noted in the FC maps. ![]() The parts of the white matter tracts, including the cerebrum and cerebellum, indicated deviations in aging trajectories in participants with schizophrenia. Our results showed that most GM regions in participants with schizophrenia in both cohorts exhibited accelerated aging, particularly in the frontal lobe, temporal lobe, and insula. The brain age gaps in different brain regions for all participants were calculated, and the differences in brain age gaps between the two groups were examined. ![]() A Gaussian process regression algorithm with fivefold cross-validation was used to train 90, 90, and 48 models for gray matter (GM), functional connectivity (FC), and fractional anisotropy (FA) maps in the training dataset, respectively. Next, we investigated the differences in brain age gaps between participants with schizophrenia and HCs from two independent cohorts. The data of 230 healthy controls (HCs) were used for model training. Here, we constructed brain-age prediction models with multimodal MRI and examined the deviations of aging trajectories in different brain regions of participants with schizophrenia recruited from multiple centers. Although many studies on brain-age prediction in patients with schizophrenia have been reported recently, none has predicted brain age based on different neuroimaging modalities and different brain regions in these patients. ![]()
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