Predicting Genetic Diseases with Machine Learning: An Analysis Using Random Forest Methodology

Swedha M ,Sakthivel S ,Nithiga RV

Department of Information Technology, Agni College of Technology, India

IJTCSE-ISSN 2349–1582

Volume No :10, Issue:02

Accepted for June 2023 Issue

ABSTRACT

In recent years, there has been growing interest in the use of gene expression programming (GEP) for data mining and optimization problems. GEP is a type of genetic programming that uses a combination of linear and non-linear functions to create models that can accurately predict complex relationships between variables. One area where GEP has shown promise is in the mining of implicit equations from data. Implicit equations are equations that are not explicitly defined, but instead, are derived from the relationships between variables. GEP can be used to identify these implicit equations by analyzing the input and output data and iteratively generating and testing different equation The process of mining implicit equations from data using GEP involves several steps. First, the input and output data are pre processed and normalized to ensure consistency and accuracy. Next, a population of candidate equations is generated using GEP, and each equation is evaluated based on its fitness to the data. The most promising equations are selected for further refinement and testing, and the process is repeated until a suitable equation is found. Once an equation is identified, it can be used to make predictions about new data and provide insight into the underlying relationships between variables. Overall, the use of GEP for mining implicit equations from data shows great promise for a range of applications, including data analysis, prediction, and optimization. However, further research is needed to fully explore the potential of this approach and refine the techniques used for equation generation and evaluation.