Table 1 Summary of the state-of-the-art literature.

Yadav and Pal³¹

UCI Repository

J48, Decision Stump, REP, RF, Gradient Boosting, AdaBoost M1, XGBoost

RF (Parallel) = 100%, XGBoost (Sequential) = 98.05%

Limited to ensemble methods

Explore hybrid models combining sequential and parallel approaches

Kumari, Kumar, and Mittal³⁹

PIMA diabetes dataset (UCI)

RF, Logistic Regression, Naive Bayes

79.08% (PIMA), 97.02% (breast cancer)

Focused on soft voting ensembles

Extend to other medical datasets and more classifiers

Tewari and Dwivedi³²

UCI dataset

JRIP, OneR, Decision Table, Boosting, Bagging

Bagging = 98%

Limited feature selection methods

Investigate more advanced feature selection techniques

Ghosh et al. 2021

PIMA Indians diabetes dataset

Gradient Boosting, SVM, AdaBoost, RF with and without MRMR feature selection

RF = 99.35% with MRMR

High complexity in MRMR feature selection process

Simplify feature selection and test on other datasets

Atif, Anwer, and Talib⁴⁴

PIMA Indians dataset, Early-Stage Diabetes

Hard Voting Classifier (Logistic Regression, Decision Tree, SVM)

81.17% (PIMA), 94.23% (Early-Stage Diabetes)

Limited voting scheme to hard voting

Explore soft voting or weighted voting for improved results

Rashid, Yaseen, Saeed, and Alasaady [45]

PIMA Indians Diabetes Dataset (PIDD)

Decision Tree, Logistic Regression, KNN, RF, XGBoost

81% after standardization and imputation

Focused only on ensemble voting techniques

Test other ensemble strategies such as bagging or boosting

Zhou, Xin, and Li ⁴⁶

PIMA Indian diabetes dataset

Boruta feature selection, K-Means + + clustering, stacking ensemble learning

98%

High computational cost in clustering

Reduce computation and test scalability

Kawarkhe and Kaur⁴⁷

PIMA Indians

CatBoost, LDA, LR, RF, GBC with preprocessing techniques

90.62%

Limited to specific preprocessing techniques

Broaden the preprocessing techniques and methods

Reza, Amin, Yasmin, Kulsum, and Ruhi⁴⁸

PIMA Indian diabetes dataset, local healthcare

Stacking ensemble with classical and deep neural networks

77.10% (PIMA), 95.50% (simulation)

Limited to stacking approaches

Explore other ensemble methods or hybrid approaches

Thongkam et al.¹⁷

Breast Cancer Dataset

AdaBoost

Improved prediction and diagnosis

Initially used only for breast cancer

Extend to other medical conditions

Velu and Kashwan¹⁸

Various Datasets

SVM, Radial Basis Function, Multi-Layer Perceptron, and Multi-Level Counter Propagation Network.

High accuracy in various applications

Complexity in model selection

Test different combinations and optimizations

Temurtas et al.¹⁹

PIMA-diabetes illness dataset

Multilayer Neural Network

Improved accuracy

Focused on PIMA dataset

Apply to other chronic disease datasets

Ayo at al.²⁰

Heart Disease Dataset

Levenberg–Marquardt approach, Probabilistic Neural Network, Naive Bayes, SVM

High accuracy in diagnosing cardiac disease

Limited to cardiac disease prediction

Broaden to include other comorbidities

Farvaresh and Sepehri²¹

Various medical datasets

Decision Tree C4.5, Bagging with C4.5, and Naive Bayes.

Improved prediction of cardiac illness

Initial focus on cardiac illness

Expand to other diseases and datasets

Kalman Filter Theory²²

PIMA Indian dataset

Adaptive and personalized insulin recommendation

Enhanced classification accuracy

Focused on insulin recommendation systems

Broaden to other therapeutic recommendations

Ajagbe et al.²³

Various applications

Multimedia analytic techniques, meta-data annotation, MPEG-7

Improved semantic analysis

Limited to MPEG-7

Explore alternative multimedia retrieval frameworks

Gong and Kim,²⁴

Misbalanced Datasets

RHS-Boost for balanced classification

High accuracy and prediction

Designed for misbalanced datasets

Apply to other datasets and test alternative balancing methods

Purnami et al.²⁵

Diabetes detection

ANFIS and PCA

Enhanced detection

Initial partitioning approach

Broaden to include other feature extraction methods

Rani and Jyothi²⁶

Diabetes dataset

Bayesian Classification, J48, KNN, Filtered Classifier, ANN, Naive Bayes

77.01% accuracy

Lack of cross-validation

Implement cross-validation and expand dataset usage

Zheng et al.²⁷

Various datasets

KNN, Naive Bayes, Decision Tree, RF, SVM, Logistic Regression

Improved recall and accuracy

Filtering criteria could be improved

Enhance feature selection and parameter tuning

Komi et al.²⁸

Sample datasets

ELM, ANN, LR, GMM, SVM

Better accuracy with fewer samples

Less amount of sample data

Increase sample data and test on more complex datasets

Sai et al.²⁹

Diabetes dataset

Weighted voting approach for ensemble prediction models

Enhanced predictive performance

Focused on ensemble prediction model

Explore ensemble expansion and optimization

Rustam et al.⁵⁶

Multiple datasets

Ensemble of CNN and LSTM for feature extraction, Random Forest model for prediction

Accuracy score of 0.99 using CNN-LSTM features with Random Forest

Limited by dataset size, generalizability issues in existing approaches

Explore other ensemble models, improve dataset diversity, real-world applicability

Faustin and Zou et al.⁵⁷

Pima Indian Diabetes Dataset

Genetic Algorithm (GA) enhanced with a two-step crossover operator for feature selection.

Accuracy: 97.5%, Precision: 98%, Recall: 97%, F1-score: 97%

Premature convergence due to insufficient population diversity in GA.

Apply the improved GA to other datasets, refine crossover technique.

Reza et al.⁵⁸

PIMA Indian Diabetes dataset, Local healthcare data

Stacking ensemble method combining classical and deep neural network models for diabetes classification.

Stacking ensemble with NN architectures: Accuracy 95.50%, Precision 94%, Recall 97%, F1-score 96%

Limited to dataset used in the study; may need more diverse data for generalization.

Explore further with other datasets, and apply to real-time healthcare systems.

Saihood and Sonuç⁵⁹

Pima Indians Diabetes Database

Ensemble machine learning models: Bagging, boosting, and stacking with hyperparameter tuning and data preprocessing.

Stacking (RF & SVM): 97.50% accuracy, Bagging (RF): 97.20%, Boosting (XGB): 97.10%

Framework limited to Pima Indians dataset; real-world data may vary.

Extend framework to include more diverse datasets and explore real-time applications in healthcare.

Daza et al.⁶⁰

Diabetes Dataset (768 patient records)

Stacking ensemble approach using 7 base algorithms; oversampling to balance the dataset and cross-validation for model training.

Best accuracy: 91.5%, Sensitivity: 91.6%, F1-Score: 91.49%, Precision: 91.5%, ROC Curve: 97%.

Performance depends on the dataset and oversampling method.

Improve the model’s generalizability by testing on other datasets and enhancing model