Towards Precision Medicine: Nature-Inspired Optimization Algorithms for Personalized Cholesterol Disease Prediction

This survey article presents a number of optimization algorithms based on the natural selection with the help of collecting data on the precision medicine and cholesterol disease prediction. Of course, it refers to the fact that traditional measures go only down to individual differences. This looks into contemporary methods for enhancing the predictive capacity; these include ant colony optimization algorithm, simulated annealing, particle swarm optimization and genetic optimization. This paper examines current strategies for cholesterol levels prediction with emphasis having been placed on creating individualized treatments based on patient parameters. The comparison helps to identify that algorithms derived from nature are more effective compared to the other approaches and methodologies. The study is useful for the researchers and healthcare practitioners who are looking for the use of computational approaches in designing individual cholesterol treatment. Therefore it carries out the possible ethical problems as well as the limitation particularly the data security concern and the future recommendation for the research.