Seizing Requirements Engineering Issues through Supervised Learning Techniques

Gramajo, María Guadalupe; Ballejos, Luciana C.; Ale, Mariel Alejandra

doi:10.1109/tla.2020.9099757

Cited by 12 publications

(7 citation statements)

References 48 publications

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“…A structured definition of supervised machine learning can be 'A supervised machine learning is a process of furnishing the input data along with the accurate output result for the given data to the machine'. A supervised machine learning model aims to find a mapping function to map the provided input with the desired/needed output (Gramajo, Ballejos, & Ale, 2020).…”

Section: Supervised Machine Learning Algorithmsmentioning

confidence: 99%

The diabacare cloud: predicting diabetes using machine learning

Alam,

Khan,

Alam

et al. 2023

Acta Sci Technol

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Machine learning (ML) is the buzz all around the technology industry and is illuminating each and every sector of human lives, be it, healthcare, finance, bioinformatics, data science, mechanical engineering, agriculture or even smart cities nowadays. ML consists of supervised and unsupervised techniques. Due to the availability of data in abundance, supervised ML has been the most preferred method in the field of data mining. In this research paper, a publicly available dataset for diabetes detection is tested to understand the efficiency of classification of a number of supervised ML algorithms to find the most accurate model. The dataset consisted of data of 768 persons out of which 500 were control and 268 were patients we found that the Random Forest algorithm outperformed the other 6 classification algorithm. In the first iteration, the Random Forest algorithm reached 78.44% accuracy. The tweaks performed in the paper outclassed the original random forest algorithm with a difference of 1.08% reaching a score of 79.52%. Further, iteration I gave 171 whilst iteration II gave 173 correct predictions out of the total 218 test data.

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Section: Supervised Machine Learning Algorithmsmentioning

confidence: 99%

The diabacare cloud: predicting diabetes using machine learning

Alam,

Khan,

Alam

et al. 2023

Acta Sci Technol

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“…Taking into consideration the comparative analysis from Table 2 for supervised machine learning classifiers, four machine learning classifiers viz. Random Forest (RF), Support Vector Machine(SVM), Naïve Bayes (NB) and Logistic regression(LR) [13][14][15][16][17] were used for supervised classification using Machine Learning [2,18,19] on the above datasets. The train test method with Stratified Crossfold with k=10 strategy was used for classifier experimentation.…”

Section: Datasets Usedmentioning

confidence: 99%

Healthcare Critical Diagnosis Accuracy

Pankaj Javale,

Desai

2023

Int. j. electr. comput. eng. syst. (Online)

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Since at least a decade, Machine Learning has attracted the interest of researchers. Among the topics of discussion is the application of Machine Learning (ML) and Deep Learning (DL) to the healthcare industry. Several implementations are performed on the medical dataset to verify its precision. The four main players, True Positive (TP), True Negative (TN), False Positive (FP), and False Negative (FN), play a crucial role in determining the classifier's performance. Various metrics are provided based on the main players. Selecting the appropriate performance metric is a crucial step. In addition to TP and TN, FN should be given greater weight when a healthcare dataset is evaluated for disease diagnosis or detection. Thus, a suitable performance metric must be considered. In this paper, a novel machine learning metric referred to as Healthcare-Critical-Diagnostic-Accuracy (HCDA) is proposed and compared to the well-known metrics accuracy and ROC_AUC score. The machine learning classifiers Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), and Naive Bayes (NB) are implemented on four distinct datasets. The obtained results indicate that the proposed HCDA metric is more sensitive to FN counts. The results show, that even if there is rise in %FN for dataset 1 to 10.31 % then too accuracy is 83% ad HCDA shows correlated drop to 72.70 %. Similarly, in dataset 2 if %FN rises to 14.80 for LR classifier, accuracy is 78.2 % and HCDA is 63.45 %. Similar kind of results are obtained for dataset 3 and 4 too. More FN counts result in a lower HCDA score, and vice versa. In common exiting metrics such as Accuracy and ROC_AUC score, even as the FN count increases, the score increases, which is misleading. As a result, it can be concluded that the proposed HCDA is a more robust and accurate metric for Critical Healthcare Analysis, as FN conditions for disease diagnosis and detection are taken into account more than TP and TN.

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“…Comparative analysis is done for all above datasets with all different experimentation strategies. Machine learning experimentation was done based considering metrics [25]- [27] AUC score, accuracy, F1 score, precision, recall, train time and test time. As per the literatures studied [12]- [14] ROC_AUC_Score is a robust metric for imbalanced dataset as compared to other metrics like accuracy_score, F1-Score, precision and recall.…”

Section: Comparative Analysismentioning

confidence: 99%

“…The implementation done by authors in paper [25] only focuses on different classifier cumulative scores, while the proposed ensemble approach takes into consideration different machine learning classifiers, evaluation methods like cross fold, stratified cross fold, train test and repeat train test. This multi-step ensemble gives more accurate results.…”

Section: Comparison With the Existing Implementationsmentioning

confidence: 99%

Machine learning ensemble approach for healthcare data analytics

Javale

Desai²

2022

IJEECS

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In healthcare machine learning is used mainly for disease diagnosis or acute condition detection based on patient data analysis. In the proposed work diabetic patient dataset analysis is done for hypoglycemia detection which means the lowering of blood glucose level. Often in healthcare it is observed that the dataset is imbalanced. Therefore an Ensemble Approach using imbalanced dataset techniques Synthetic Minority Over-sampling Technique and Adaptive Synthetic oversampling methods with different evaluation methods like train-test, k-fold, Stratified K-Fold and repeat train-test were used. This ensemble approach was implemented on diabetic dataset using K-Nearest Neighbor, Support Vector Machine, Random Forest, Naïve Bayes and Logistic Regression classifiers with average Stacking-C method thereafter to conclude. Comparative analysis was done using three different considerations. The results showed that KNN and Random forest gives more stable metric values both on balanced and imbalanced dataset. The confusion matrix consideration concluded that KNN and Random Forest were found to be better with least false negative and maximum true positive count. But if average train and test time is taken into consideration then Naïve Bayes and Random forest had least average train-test time. Thus the three different considerations concluded that the proposed ensemble approach gives better clarity for different classifier implementation using machine learning.

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Seizing Requirements Engineering Issues through Supervised Learning Techniques

Cited by 12 publications

References 48 publications

The diabacare cloud: predicting diabetes using machine learning

The diabacare cloud: predicting diabetes using machine learning

Healthcare Critical Diagnosis Accuracy

Machine learning ensemble approach for healthcare data analytics

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