Table 1 Analysis of data mining methods for the above studies
From: Detecting diseases in medical prescriptions using data mining methods
No. | Authors | Year/Title | Journal | Proposed data mining algorithm | Measurement criteria (%) |
|---|---|---|---|---|---|
1 | Kondababu, A., et al. [9] | 2021/A comparative study on machine learning based heart disease prediction | Materials Today: Proceedings | HRFLM (RF + LM) | Accuracy = 88.7% |
2 | Jeyaranjani, J., T. Dhiliphan Rajkumar, and T. Ananth Kumar [10]. | 2021/Coronary heart disease diagnosis using the efficient ANN model | Materials Today: Proceedings | ANN | Accuracy = 97% |
3 | Jothi, K. Arul, et al. [11] | 2021/Heart disease prediction system using machine learning | Materials Today: Proceedings | Decision Tree | Accuracy = 81% |
4 | Pavithra, V., and V. Jayalakshmi [12]. | 2021/Hybrid feature selection technique for prediction of cardiovascular diseases | Materials Today: Proceedings | HRFLC (RF + ADABOOST + Pearson Coefficient) | – |
5 | Ramesh, G., et al. [13] | 2021/Improving the accuracy of heart attack risk prediction based on information gain feature selection technique | Materials Today: Proceedings | SVM و RF | Accuracy = 88% |
6 | Maini, Ekta, et al. [14] | 2021/Machine learning--based heart disease prediction system for Indian population: An exploratory study done in South India | Medical Journal Armed Forces India | RF | Accuracy = 93.8% Sensitivity = 92.8% Specificity = 94.6% |
7 | Kumar, Santosh, and G. Sahoo [15]. | 2015/Classification of heart disease using Naive Bayes and genetic algorithm | Computational Intelligence in Data Mining | Naïve Bayes and Genetic | – |
8 | Jain, Bhavini, et al. [16] | 2021/A machine learning perspective: To analyze diabetes | Materials Today: Proceedings | Neural network | Accuracy = 87.88% |
9 | Kumari, Saloni, Deepika Kumar, and Mamta Mittal [17]. | 2021/An ensemble approach for classification and prediction of diabetes mellitus using soft voting classifier | International Journal of Cognitive Computing in Engineering | Soft voting classifier | Accuracy = 79.08% |
10 | Khaleel, Fayroza Alaa, and Abbas M. Al-Bakry [18]. | 2021/Diagnosis of diabetes using machine learning algorithms | Materials Today: Proceedings | Logistic regression | Accuracy = 94% |
11 | Arumugam, K., et al. [19] | 2021/Multiple disease prediction using Machine learning algorithms | Materials Today: Proceedings | Decision Tree | – |
12 | Wei, Xiaoxia, et al. [20] | 2021/Developing and validating a prediction model for lymphedema detection in breast cancer survivors | European Journal of Oncology Nursing | Logistic regression | AUC = 0.889 (0.840–0.938), sensitivity = 0.771, specificity = 0.883, accuracy = 0.825, and Brier scores = 0.141 |
13 | Dhanya, R., et al. [21] | 2020/F-test feature selection in Stacking ensemble model for breast cancer prediction | Procedia Computer Science | Stacking | – |
14 | Onan, Aytuğ [22]. | 2015/A fuzzy-rough nearest neighbor classifier combined with consistency-based subset evaluation and instance selection for automated diagnosis of breast cancer | Expert Systems with Applications | fuzzy-rough nearest neighbors, consistency based subset evaluation, and fuzzy-rough instance selection | Accuracy = 99.71% |
15 | Ferdowsy, Faria, et al. [23] | 2021/A machine learning approach for obesity risk prediction | Current Research in Behavioral Sciences | Logistic regression | Accuracy = 97.09% |
16 | Pinto, Ana, et al. [24] | 2020/Data mining to predict early stage chronic kidney disease | Procedia Computer Science | J48 | Accuracy = 97.66% Sensitivity = 96.13% Specificity = 98.78% Precision = 98.31% |
17 | Ahsani-Estahbanati, Ehsan, et al. [25] | 2021/Incidence rate and financial burden of medical errors and policy interventions to address them: a multi-method study protocol | Health Serv Outcomes Res Method | Delphi method | – |
18 | Malladi, Ravisankar, Prashanthi Vempaty, and Vyshnavi Pogaku [26]. | 2021/Advanced machine learning based approach for prediction of skin cancer | Materials Today: Proceedings | CNN | Accuracy = 84.5% |
19 | Dehkordi, Shiva Kazempour, and Hedieh Sajedi [27]. | 2019/Prediction of disease based on prescription using data mining methods | Health and Technology | Staking | Accuracy (label 1) =73.17% Accuracy (label 2) =57% |
20 | Teimouri, Mehdi, et al. [28] | 2016/Detecting Diseases in Medical Prescriptions Using Data Mining Tools and Combining Techniques | Iranian journal of pharmaceutical research: IJPR | Weighted Voting | Accuracy = 97.16% |
21 | Trasierras, Antonio Manuel, José María Luna, and Sebastián Ventura [29]. | 2022/Improving the understanding of cancer in a descriptive way: An emerging pattern mining-based approach | International Journal of Intelligent Systems | AN APPROACH BASED ON EPM | – |
22 | Frias, Mario, et al. [30] | 2021/ Classification Accuracy of Hepatitis C Virus Infection Outcome: Data Mining Approach | Journal of Medical Internet Research | partial decision trees, Ensemble | Sensitivity = 84.3% Specificity = 83.7% AUROC = 0.89 |