Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Logistic regression is one core predictive modeling technique that has been used extensively in health and biomedical problems. Recently a lot of research has been focusing on enforcing sparsity on the learned model to enhance its effectiveness and interpretability, which results in sparse logistic regression model. Hover, no matter the original or sparse logistic regression, they require the inputs to be in vector form. This limits the applicability of logistic regression in the problems when the data cannot be naturally represented vectors (e.g., functional magnetic resonance imaging and electroencephalography signals). To handle the cases when the data are in the form of multi-dimensional arrays,  propose  MulSLR : Multilinear Sparse Logistic Regression.  MulSLR  can be vied as a high order extension of sparse logistic regression. Instead of solving one classification vector as in conventional logistic regression,  solve for  K  classification vectors in  MulSLR  ( K  is the number of modes in the data).  propose a block proximal descent approach to solve the problem and prove its convergence. The convergence rate of the proposed algorithm is also analyzed. Finally  validate the efficiency and effectiveness of  MulSLR  on predicting the onset risk of patients with Alzheimer's disease and heart failure.
['Fei Wang', 'Ping Zhang', 'Buyue Qian', 'Xiang Wang', 'Ian Davidson']
I am writing a research paper on a idea Clinical risk prediction with multilinear sparse logistic regression
[{"from": "human", "value": "I am writing a research paper on a idea Clinical risk prediction with multilinear sparse logistic regression"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Logistic regression is one core predictive modeling technique that has been used extensively in health and biomedical problems. Recently a lot of research has been focusing on enforcing sparsity on the learned model to enhance its effectiveness and interpretability, which results in sparse logistic regression model. Hover, no matter the original or sparse logistic regression, they require the inputs to be in vector form. This limits the applicability of logistic regression in the problems when the data cannot be naturally represented vectors (e.g., functional magnetic resonance imaging and electroencephalography signals). To handle the cases when the data are in the form of multi-dimensional arrays,  propose  MulSLR : Multilinear Sparse Logistic Regression.  MulSLR  can be vied as a high order extension of sparse logistic regression. Instead of solving one classification vector as in conventional logistic regression,  solve for  K  classification vectors in  MulSLR  ( K  is the number of modes in the data).  propose a block proximal descent approach to solve the problem and prove its convergence. The convergence rate of the proposed algorithm is also analyzed. Finally  validate the efficiency and effectiveness of  MulSLR  on predicting the onset risk of patients with Alzheimer's disease and heart failure."}]