## 2 Literature Survey
A hybrid combination of the PCA-LDA model was presented in [37], for the classification of neuron-generative diseases. Statistical attributes were ranked and optimized, and it achieved a credible accuracy rate. The authors in [38], discussed a Sequential Forward Selection (SFS) approach using 10-fold cross validation on a lung disease dataset. It was validated with SVM for classifying and it showed an impressive result with the lowest execution time on an even larger set of data samples. An extensive literature review has been carried out to analyse the existing studies associated with the scope of this research. Different authors have presented numerous attribute selection techniques for disease diagnosis. Similarly, common wrapper methods such as BFS, GSS,Particle swarm optimization and genetic algorithm have also been discussed. The relevant studies on the use of wrappers in the classification of diseases are summarized in Table 1.  
Furthermore, numerous filter methods such as One-R, Relief-F, Chi-square, CFS and Gain ratio have been used in different studies. Based on different datasets, appropriate filter techniques have been employed to evaluate the goodness of attribute set. Table 2 highlights some vital research works using filter approach of attribute selection process. Our extensive literature survey highlights some important research in the field of disease diagnosis using machine learning. Several researchers and academicians used different computational techniques \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Valueable Research Contribution** & **Wrapper Method Used** & **Datasets Used** & **Year** \\ \hline [PERSON] [39] & Genetic algorithm & Dengue datasets & 2011 \\ [PERSON] et al. [40] & Sparse coding approach & Knowledge based datasets & 2012 \\ [PERSON] et al. [41] & GLCM and intensity histogram & Kidney ultrasound images & 2012 \\ [PERSON] [42] & Modified Binary Particle Swarm & Several disease datasets & 2013 \\ [PERSON] [43] & Artificial Bee Colony algorithm & Gene expression data & 2013 \\ [PERSON] [44] & Genetic algorithm & Coronary artery disease & 2013 \\ [PERSON] et al. [45] & Rough set theory & Heterogeneous medical & 2014 \\ [PERSON] et al. [46] & Improved version of bat algorithm & ECG signals data & 2015 \\ [PERSON]. [47] & Particle Swarm Optimization (PSO) & Liver disease & 2018 \\ [PERSON] et al. [48] & Particle Swarm Optimization and & Breast cancer and hepatitis & 2018 \\ [PERSON] et al. [49] & Ant colony optimization & Face recognition dataset & 2008 \\ [PERSON] et al. [50] & Genetic algorithm & EEG Signal & 2015 \\ [PERSON] et al. [51] & Support vector machine & Cancer dataset & 2002 \\ [PERSON] et al. [52] & Artificial Bee Colony Algorithm & Liver, diabetes and hepatitis & 2013 \\ [PERSON] et al. [53] & linear discriminant analysis & Hepatitis datasets & 2009 \\ [PERSON] et al. [54] & Particle swarm optimization & obstructive sleep apnea & 2012 \\ [PERSON] et al. [55] & Artificial bee colony & Heterogeneous medical & 2011 \\ [PERSON]. [56] & Enhanced Genetic Algorithm & Diabetes datasets & 2020 \\ [PERSON]. [57] & Best first search method & Hepatitis datasets & 2020 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: An outline of relevant study on wrapper based attribute selection.