Research Article
Hybrid Model for Detection of Cervical Cancer Using Causal Analysis and Machine Learning Techniques
Table 3
Comparison of a research review based on machine learning methods.
| Article | Technique utilizes | Type of cancer | Important feature discussed | Dataset used | Validation technique |
| [47] | Artificial neural network | Cancer in breast | Age and mammography results | Diagnostics data and pathological data | Crossvalidation 10-fold | [48] | Support vector machine | Cancer multiple myeloma | STAT1, BRCA1, and CCND1 CCNB1 | Online UCI | Crossvalidation 20-fold validation | [49] | Random forest | Cervical cancer | Diet, eating habits, and BME | Clinical data | Crossvalidation 10-fold | [50] | BN methods | Lung cancer | BP, age, and other parameters | Kaggle online dataset | 10-fold crossvalidation | [51] | SVM | Cervical cancer, breast cancer | Skin type, breast size, and skin color | Dataset from the hospital (China) | Clinical survey data | [52] | Boruta | Cervical cancer, lung and breast | Age, infection type | Clinical survey data | Crossvalidation | [53] | SVM with random forest | Cervical cancer, cancer in lungs | BME | UCI online dataset | 10-fold crossvalidation | [54] | K-NN, SVM | Cervical cancer | Age and mammography results | UCI dataset | Crossvalidation 10-fold |
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