Data Mining Using Random Forest, Naïve Bayes, and Adaboost Models for Prediction and Classification of Benign and Malignant Breast Cancer

Imran, Bahtiar; Hambali, Hambali; Subki, Ahmad; Zaeniah, Zaeniah; Yani, Ahmad; Alfian, Muhammad Rijal

doi:10.33480/pilar.v18i1.2912

Cited by 2 publications

(2 citation statements)

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“…Then, a machine learning model, such as Random Forest, Naive Bayes, or SVM, is trained on a labeled dataset of resumes and their corresponding job domains. The trained model is used to predict job domains for each pre-processed resume, and the accuracy of the model is evaluated using metrics such as precision, recall, and F1-score (21) .…”

Section: Proposed Approachmentioning

confidence: 99%

Automated Resume Parsing and Job Domain Prediction using Machine Learning

Sinha¹,

Akhtar²,

Kumar³

2023

IJST

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Objectives: This study aims to develop an efficient approach for parsing resumes and predicting job domains using natural language processing (NLP) techniques and named entity recognition to enhance the resume screening process for recruiters. Methods: The proposed approach involves preprocessing steps, such as cleaning, tokenization, stop-word removal, stemming, and lemmatization, implemented with the PyMuPDF and doc2text Python modules. Regular expressions and the spaCy library are utilized for entity recognition and name extraction. The model achieved a prediction accuracy of 92.08% and an F1-score of 0.92 on a dataset of 1000 resumes. An ablation experiment assessed the contributions of different factors. Findings:The approach demonstrated a high prediction accuracy of 92.08% and F1-score of 0.92 for job domain prediction, effectively identifying relevant job domains from resumes. Evaluations on individual job domains showed excellent precision and recall scores, validating its applicability. Preprocessing techniques significantly improved accuracy, while the integration of regular expressions and spaCy enhanced the model's performance. This approach automates resume screening, reducing recruiters' workload, saving time and effort, and improving candidate selection and the hiring process. Novelty: This study introduces a novel approach combining NLP techniques, regular expressions, and entity recognition for resume parsing and job domain prediction. This integration enhances accuracy and efficiency, offering a unique solution for resume screening.

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Section: Proposed Approachmentioning

confidence: 99%

Automated Resume Parsing and Job Domain Prediction using Machine Learning

Sinha¹,

Akhtar²,

Kumar³

2023

IJST

View full text Add to dashboard Cite

show abstract

“…The tools used to process the data store were using Orange Data Mining Tools. Orange Data Mining was a data mining tool that gave better results than other tools [24]- [27]. Figure 4 shows the flow of the classification process.…”

Section: Classificationmentioning

confidence: 99%

Classification of stroke patients using data mining with adaboost, decision tree and random forest models

et al. 2022

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A stroke is a fatal disease that usually occurs to the people over the age of 65. The treatment progress of the medical field is growing rapidly, especially with the technological advance, with the emergence of various medical record data sets that can be used in medical records to identify trends in these data sets using data mining. The purpose of this study was to propose a model to classify stroke survivors using data mining, by utilizing data from the kaggle sharing dataset. The models proposed in this study were AdaBoost, Decision Tree and Random Forest, evaluation results using Confusion Matrix and ROC Analysis. The results obtained were that the decision tree model was able to provide the best accuracy results compared to the other models, which was 0.953 for Number of Folds 5 and 10. From the results of this study, the decision tree model was able to provide good classification results for stroke sufferers.

show abstract

Data Mining Using Random Forest, Naïve Bayes, and Adaboost Models for Prediction and Classification of Benign and Malignant Breast Cancer

Cited by 2 publications

References 10 publications

Automated Resume Parsing and Job Domain Prediction using Machine Learning

Automated Resume Parsing and Job Domain Prediction using Machine Learning

Classification of stroke patients using data mining with adaboost, decision tree and random forest models

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