An efficient dimensionality reduction framework using metaheuristic optimization with deep learning models for...

Amyotrophic lateral sclerosis (ALS) is a disastrous neuro-degenerative infection which affects motor neuron inhabitants of the spinal cord, brainstem, and cerebral cortex, resulting in progressive disorder and demise from respiratory difficulty. ALS is considerably assorted disorder comprising symptoms such as muscle weakness, difficulty in swallowing, speaking, breathing, and changes in mental and emotional health. Hence, this disease requires more beneficial medication and also, successful treatment is affected by heterogeneous disease development, resulting in issues with patient stratification. Recently, many researches have been published by using deep learning (DL) and machine learning (ML) methods and, more commonly, artificial intelligence (AI). This paper presents a Dimensionality Reduction Framework Using Metaheuristic Optimization with Deep Learning Models for the Amyotrophic Lateral Sclerosis Disease Progression Prediction (DRMODL-ALSDP) method. The aim is to provide an effectual model for the progression prediction of ALS disease using advanced techniques. Initially, the data pre-processing stage applies min-mx normalization to transform raw data into a suitable format. Furthermore, SMOTE is employed to address class imbalance by upsampling the minority classes in disease progression stages. Furthermore, the binary swordfish movement optimization algorithm (BSMOA) technique is used for feature selection. Moreover, the hybrid of a temporal convolutional network and long short-term memory with attention mechanism (TCN-LSTM-AM) technique is employed for the classification process. Finally, the marine predator's algorithm (MPA) technique optimally fine-tunes the hyperparameter values and improves classification performance. A widespread simulation is performed to verify the performance of the DRMODL-ALSDP model. The comparison study of the DRMODL-ALSDP model accentuated the superior accuracy output of 98.17% over existing methods.