Classification of Diabetes Dataset with Data Mining Techniques by Using WEKA Approach

Multi-Tier Ensemble Learning Model With Neighborhood Component Analysis to Predict Health Diseases

“…Their developed model achieved the performance regarding precision values: 94.49%, 93.57%, 75.6%, and 100%, recall values: 98.62%, 84.89%, 81.78%, and 100%, F-measure values: 96.32%, 88.8%, 77.12%, and 100%, accuracy values: 96.74%, 85.73%, 75.78%, and 100%, and AUC values: 99%, 87%, 76%, and 100%, for datasets 1, 2, 3, 4, respectively. However, Alpan and Ilgi [13] employed various ML models to perform an early-stage diabetes risk prediction dataset to predict diabetes. Their employed model: KNN, received an accuracy value of 98.07% while predicting diabetes disease.…”

“…Their developed model generated an accuracy of 98% while predicting chronic liver disease. After examining the prior investigations, it has been revealed that various past studies included either for generating various dimension reduction approaches: attribute permutation and hierarchical clustering approach, binary firefly algorithm, cooperative coevolution technique, LDA, NCA, ReliefF, Chaotic Darcy optimization, CSO, KH, BFO included in [22], [28], [30], [32], [33], [34], [36], or employing various single ML classifiers: DT, SVM, RF, MLP, NB, LR, KNN, XGB, LGBM, SVM-linear included in [5], [9], [13], [14], [16], [17], [18], [21], [23], or developing several combined approaches consisting of various outlier detection and removal approaches along with the imbalance learning algorithms: cluster-based oversampling technique, DBSCAN with SMOTE, Isolation forest with SMOTETomek, IQR algorithm with SMOTE, Instance selection with SMOTE included in [10], [11], [12], [19], [26], or utilizing only single imbalance learning algorithms: SMOTE, SVM-SMOTE included in [20], [24], [25], [27], or implementing hyperparameter optimization strategies: 2level genetic optimizer with c-type SVM, LR with GA optimization strategy included in [29], [35], or implementing several DL-enabled techniques: Conv-LSTM, deep extreme learning model, LSTM, ANN, deep neural network, MLP, convergent artificial intelligence model, deep convolutional neural network, successive encoder-decoder approach, VAE, CLUSTIMP included in [2], [3], [37], [38], [39], [40], [41], [42], [44], [45], [46], [47], [48],…”

“…In this proposed research, the performance of the suggested MTWEL approach relies on the WV technique is compared to the previous essential works presented in Table 11 for the prediction of diabetes disease and HCC survival. Several past research works are based on either traditional ML techniques such as employing single powerful ML classifiers included in [9], [13], [5], implementing various dimension reduction techniques included in [28], [30], [32],…”

“…Several ML-based solutions were proposed to predict diabetes disease and HCC survival, and we can categorize these solutions based on the limitations mentioned. The proposed method is employed to solve the problems while degrading the performance conducted by the single powerful classification models, such as NB, LR, RF, KNN, DT, SVM, MLP includes [9], [13], [5], methods that employ dimension reduction techniques; namely, binary firefly algorithm, cooperative coevolution technique, LDA, NCA, ReliefF, Chaotic Darcy optimization, CSO, KH, BFO, includes [28], [30], [32], [33], [34], [36], methods that utilize various hyperparameter optimization strategies, such as, 2-level genetic optimizer with c-type SVM, genetically optimized LR model includes [29], [35], methods that employ several imbalanced learning approaches, namely, cluster-based oversampling technique (a neural network with Augmented sets technique), SMOTE includes [10], [26], [27], deep learning approaches such as MLP with GWO and Adaptive Particle swarm optimization with GWO (APGWO) techniques and ANN model includes [45], [46], and methods that utilize several EL techniques: Stacking based EL, RFGBEL models include [4], [64]. Hence, it has been declared that the suggested MTWEL approach achieved outstanding performance while comparing with the prior benchmark methods and provides the most desirable classification outcomes.…”

“…In the case of prediction of the HCC disease, the authors either employed class rebalancing algorithms: SMOTE and cluster-based oversampling technique included in [10], [26], [27] to solve the class balancing problems, or utilizes feature selections techniques: Linear Discriminant Analysis (LDA) and Neighborhood Component Analysis (NCA) and many more included in [28], [30], [32], [33], [34], [36] for the selection of informative features, or employing robust single classifiers: decision tree (DT), support vector machine (SVM), and random forest (RF) included in [5] or using methods based on ensemble models and deep learning included in [53], [4], [63], [64] to enhancement the performance of their generated models after employing HCC dataset [10]. While in case of predicting diabetes disease, several previous methodologies either employed single classification models: RF, logistic regression (LR), Naïve Bayes (NB), K-nearest neighbors (KNN) included [9], [13], or utilized deep learning-enabled feature selection techniques: multilayer perceptron (MLP) with adaptive particle swarm optimization with grey wolf optimization (APGWO) and GWO techniques included in [45] to select the optimum features, or utilized deep learning techniques: artificial neural network (ANN) included in [46] to classify the diabetes disease and enhancement the performance of their developed models after employing early-stage diabetes risk prediction dataset [9]. However, such above-specified methodologies have some lags due to generating inefficient and inaccurate outcomes.…”