## 2 Related Works  
The authors [9] proposed two preprocessing methods for missing and heterogeneous data and used k-means clustering. In [10], a dataset of 4000 chronic hepatitis C patients diagnosed at Cairo University's multidisciplinary hospital is used with linear regression. The dataset is balanced using Synthetic Minority Over-sampling Technique (SMOTE) methods. The performance of LR and Neural Networks (NN) is 75.2% and 73%, respectively. CART, ADTree, and REP-Tree models give an excellent area under the receiver-operating-characteristic curve (AUROC), ranging between 95.5% and 99%. The high accuracy of HCC diagnosis ranges between 93.2% and 95.6%. According to [PERSON] and [PERSON] [11], LR is the best-known machine-learning model for binary classification. The authors proposed three experiments: genetically selecting logistic-regression parameters, selecting features, and training a logistic-regression model with genetically determined coefficients (weights) with an F1-score of 93.56%. Iterative optimization is employed to optimize the logistic-regression coefficients. First, Chronic Kidney Disease data is preprocessed by employing missing data-handling methods with mean and mode of statistical analysis [12]. Then, Recursive Feature Elimination (RFE) is applied to select the best relevant features. In that analysis, the classification algorithms used are SVM, k-NN, DT, and RF. Random Forest surpassed all competing algorithms with 100% accuracy based on all measures. For prediction, the author [13] used fifteen different models for the HCC dataset. Several methods have evaluated the feature weights, including L-1 penalty or LASSO regression, L-2 penalty or ridge regression, Genetic Algorithm Optimization, and Regression Function. The RFGBEL model achieves 93.92% accuracy, 94.73% sensitivity, 93% F-1 score, a Log-Loss or cross-entropy score of 58.9%, and 72% Jaccard. Weighted Synthetic Minority Over-sampling Technique (WSMOTE) is an effective method for dealing with dataset imbalances for liver disease [14]. The Improved Fuzzy C Means clustering technique is applied to substitute missing information to increase the overall accuracy of the study. Combining Kernel Support Vector Machine and Fuzzy Convolutional Neural Network (FCNN) yielded a heterogeneous ensemble classifier that used Bootstrap aggregation to obtain accurate results. It has a classification rate of 99.12%, whereas other methods such as MCNN and FCNN have 90.75% and 92.48%, respectively. The author [15] proposed an RFE-GB algorithm for heart disease. The proposed algorithm produced the greatest results (90.7%). Furthermore, the proposed RFE-GB algorithm has a significantly higher area under the curve than other algorithms. The robustness of HCC is assessed [16] with improved Fuzzy C Means clustering to impute missing values. The optimal feature subset is selected using multiple ensemble methods. Experimental results from the proposed scheme demonstrate that FCNN is more accurate in classifying than other techniques with precision, recall, F-measure, and accuracy scores of 90.78%, 97.36%, 93.96%, and 92.48%, respectively. As a result of an inability to properly metabolize glucose, diabetes illnesses are becoming more prevalent [17]. The author in [18] proposed dynamic RFE (dRFE) for identifying the subset of features most closely associated with the class labels. The feature-selection scheme comprises RFE and L2 regularization in the wrapper-based method for diabetes, to overcome the overfitting problem. The algorithm predicts diabetes disease more accurately than other existing algorithms. In both of the datasets, GSE53045 and GSE66695, 100% accuracy is achieved. For the other three methylome datasets, GSE74845, GSE103186, and GSE80970, the best prediction accuracy obtained is 92.59%, 94.24%, and 86.01%, respectively. Cervical cancer [19] prediction utilized SMOTE to balance classes and impute missing values in data preprocessing. The firefly algorithm is applied to identify the critical features and optimize the models.