Revolutionizing ALS Assessment: XGBoost Classification with Progressive Entropy Weighted-based Focal Loss on Gene Sequences

In recent times, the global rise in the prevalence rate of amyotrophic lateral sclerosis (ALS) has profoundly affected the welfare of several people in the world. ALS is a lethal neurodegenerative disease (NDD) that damages the nerve cells in the brain and spinal cord. Moreover, it removes the person’s capability of controlling muscle movements in the body. It is necessary to detect the disease earlier, to reduce the disease severity, and to enhance the life expectancy of the patients. Traditionally, ALS screening is handled by qualified physicians through blood tests, which is an expen-sive, painful, and time-consuming process. To resolve this limitation, several researchers focused on the ALS classification. Conversely, it have a few drawbacks, such as lack of accuracy and speed, overfitting of data, and noise handling tasks. For enhancing the classification of ALS, the proposed approach employs progressive entropy weighted-based focal loss (PEWFL)-XGBoost through the Kaggle ALS dataset. The XGBoost is used for the ability to manage missing data and speed. Nevertheless, it has certain limitations such as overfitting of data, hyperparameter tuning, and handling of smaller datasets. To resolve this, PEWFL is added to the XGBoost system to improve the classification performance. Correspondingly, the efficiency of the respective system is calculated using performance metrics to evaluate the performance of the research. Moreover, internal comparison with classical algorithms such as XGBoost, K-nearest neighbor, and random forest discloses the efficacy of the respective model. The proposed system is envisioned to contribute to molecular genetics and neuroscience research and assist neurologists in enhancing the diagnosis of ALS.