Single nucleotide polymorphism based on hypertension potential risk prediction using LSTM with Adam optimizer

Abstract

Recent healthcare research has focused a great deal of interest on using genetic data analysis to predict the risk of hypertension. This paper presents a unique method for accurately predicting the vulnerability to hypertension by utilizing single nucleotide polymorphism (SNP) data. We present a novel neural network design utilizing the adaptive moment (Adam) optimizer to describe the intricate temporal correlations in SNPs. The study used a dataset with carefully preprocessed SNP data from a broad cohort for model input. The long short-term memory (LSTM) network was methodically built and trained with hyper-parameter and fine-tuning using the Adam optimizer to converge on ideal weights. Our findings indicate encouraging predictive performance, highlighting the suggested methodology’s usefulness in determining hypertension risk factors. The result showed that the proposed method achieved stability in the performance of 89% accuracy, 96% precision, 88% recall, and 92% F1-score. Due to its higher accuracy and greater predictive power, our SNP-based LSTM methodology is superior to the conventional machine learning method. By providing a novel framework that uses genetic data to predict the risk of hypertension, this research makes substantial contribution to the field of predictive healthcare. This framework helps with early intervention and customized preventative efforts.