Linear regressive weighted Gaussian kernel liquid neural network for brain tumor disease prediction using time series data - Firoz Khan; Sardar Irfanullah Amanullah; Shitharth Selvarajan (Scientific Reports)
Abstract A brain tumor is an abnormal growth of cells within the brain or surrounding tissues, which can be either benign or malignant. Brain tumors develop in various regions of the brain, each affecting different functions such as movement, speech, and vision, depending on their location. Early prediction of brain tumors is crucial for improving survival rates and treatment outcomes. Advanced techniques, including medical imaging and machine learning, are widely used for early diagnosis. However, conventional machine learning and deep learning detection models face challenges in achieving high accuracy in brain tumor disease prediction while minimizing time complexity. To address this, a novel Linear Regressive Weighted Gaussian Kernel Liquid Neural Network (LRWGKLNN) model is developed. The proposed LRWGKLNN model comprises four major steps, namely data acquisition, preprocessing, feature selection, and classification. In the initial step, a large volume of time-series data samples is collected from a comprehensive dataset. Following data collection, preprocessing is performed, involving two key processes: handling missing data and outlier detection. First, the proposed LRWGKLNN model handles missing values using a linear regression method. After the imputation process, outlier data is identified and removed using the Generalized Extreme Studentized Deviation test. Once preprocessing is complete, the Cosine Congruence Weighted Majority Algorithm is employed to select significant features from the dataset while removing irrelevant features. This step helps minimize the brain tumor disease prediction time. Finally, the classification process is performed using the selected significant features with the Gaussian Kernelized Liquid Neural Network. This approach enhances the accuracy of brain tumor disease prediction using time-series data samples. The experimental evaluation is carried out using various performance metrics such as accuracy, precision, recall, F1 score, and disease prediction time with respect to the number of time-series data samples. The obtained results demonstrate that the proposed LRWGKLNN model achieves higher 4%, 4% 5%, 4% and 4% accuracy, precision, recall, specificity and F1 score in brain tumor disease prediction. Furthermore, the LRWGKLNN model realizes a substantial reduction in time consumption with feature selection by 16% compared to existing deep learning methods.