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- W4387304311 abstract "The symptoms of bipolar disorder include extreme mood swings. It is the most common mental health disorder and is often overlooked in all age groups. Bipolar disorder is often inherited, but not all siblings in a family will have bipolar disorder. In recent years, bipolar disorder has been characterised by unsatisfactory clinical diagnosis and treatment. Relapse rates and misdiagnosis are persistent problems with the disease. Bipolar disorder has yet to be precisely determined. To overcome this issue , the proposed work Adaptive Structure Convolutional Neuron Classifier (ASCNC) method to identify bipolar disorder. The Imbalanced Subclass Feature Filtering (ISF2) for visualising bipolar data was originally intended to extract and communicate meaningful information from complex bipolar datasets in order to predict and improve day-to-day analytics. Using the Scaled Features Chi-square Testing (SFCsT), extract the maximum dimensional features in the bipolar dataset and assign weights. In order to select features that have the largest Chi-square score, the Chi-square value for each feature should be calculated between it and the target. Before extracting features for the training and testing method, evaluate the Softmax neural activation function to compute the average weight of the features before the feature weights. Diagnostic criteria for bipolar disorder are discussed as an assessment strategy that helps diagnose the disorder. It then discusses appropriate treatments for children and their families. Finally, it presents some conclusions about managing people with bipolar disorder." @default.
- W4387304311 created "2023-10-04" @default.
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- W4387304311 date "2023-01-01" @default.
- W4387304311 modified "2023-10-05" @default.
- W4387304311 title "Bipolar Disease Data Prediction Using Adaptive Structure Convolutional Neuron Classifier Using Deep Learning" @default.
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- W4387304311 doi "https://doi.org/10.1007/978-981-99-5166-6_10" @default.
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