## 3 Proposed Methodology
### Machine Learning-Based Diabetes Prediction Using Genetic Information
#### 3.1.2 Feature Extraction  
Feature extraction refers to extracting the appropriate information from the dataset. Features extraction methods reduce the number of attributes by avoiding redundant features. If there are too many relevant features, our model will perform better. There are numerous feature extraction methods, and we used discrete wavelet transform (DWT) to extract useful features and principal component analysis (PCA) to reduce the dimensionality and redundant data. Genetic features were mined by utilizing the alpha-amino carboxylic acid (amino acid) sequence grouping data of genes. Human cells are made up of enormous fractions of alpha-amino carboxylic acids that give cells their structure. Amino acid sequence information was downloaded from the publicly available online genetic databases; then, length, entropy, discrete wavelet, and principal component features were extracted from this sequence information. Each of these features is described below. Gene sequence arrangement is a mixture of diverse amino acids. Various arrangements are of dissimilar sizes. We can assess length by tallying a gene's alpha-amino carboxylic acid arrangement. So, the length is mainly the measurement of the alpha-amino carboxylic acid sequence. The entropy can be assessed by determining a particular possibility's likelihood [22]. Entropy is defined in Equation (1). \[E=-\sum_{n=1}^{20}(p_{n}\times\log 2p_{n}) \tag{1}\]  
where \(p_{n}\) is a probability of an alpha-amino carboxylic acid in a sequence. Figure 4: Proposed research methodology for diabetes prediction. A feature extraction procedure based on DWT is proposed. DWT is a powerful and popular tool that distinguishes between significant and irrelevant data without losing actual data. DWT is used to extract useful information at high speed. It reduces the resources and computation time and is also easy to implement. In our proposed method, we applied a discrete wavelet transform on extracted gene sequences by employing Python programming language using the NumPy library, which returns the detail coefficient (DC) and approximation coefficient (AC) values for each gene sequence. A gene is made up of 20 alpha-amino carboxylic acids. A vector length of 20 is attained for a solitary gene, and then a discrete wavelet transform is applied to the EIIP values of this vector [23]. There are distinct EIIP values for every alpha-amino carboxylic acid. These EIIP values choose the consistent energy conditions for the entire valance electron in the specific alpha-amino carboxylic acid. The list of alpha-amino carboxylic acids, representation codes, and EIIP values is shown in Table 2.  
In our work, the algorithm based on DWT [24] was used to perform feature extraction in which data were decomposed and divided into two parts using Equations (2) and (3). \[I_{i}^{j}=\frac{1}{2}\left(I_{2i}^{j+1}+I_{2i+1}^{j+1}\right) \tag{2}\]  
\[D_{i}^{j}=\frac{1}{2}\left(I_{2i}^{j+1}-I_{2i+1}^{j+1}\right) \tag{3}\]  
PCA is a modern data analysis tool for making predictive models [25; 26]. It is a very useful method for extracting relevant information from confusing datasets. It reduces the various actual indicators to single or more inclusive indicators. We applied PCA to our dataset to detect the correlation between variables and avoid repetitive information. The model for calculating and extracting PC factors [3] is:  
\[F_{i}=T_{i1}X_{1}+T_{i2}X_{2}+T_{ik}X_{k}(i=1,2,3,\ldots\ldots,m) \tag{4}\]  
where \(F_{i}\) is \(i\) principal component factor, \(k\) is the number of indicators, and \(m\) is the number of principal component factors. We used PCA as an unsupervised learning technique in which our dataset of interrelated variables was transformed into a novel set of variables recognized as principal components (PCs). In our work, PCA worked as a dimensionality reduction technique. Figure 5 represents the principal component analysis block diagram to discover the PCs. \begin{table}
\begin{tabular}{c c  
To standardize the dataset, Equation (5) calculates the mean and standard deviation [27].