An Intelligent System for Predicting Breast Cancer (ISPBC) using a Novel Feature Selection Technique

2025-11-21T20:02:56+07:00

Breast cancer (BC) is becoming a global epidemic, largely affecting women. Breast cancer cases keep climbing steadily. Thus, early detection technologies or systems that notify patients to this disease are essential. Individuals can start treatment for this life-threatening illness, so that patients may be cured or given longer lives. To achieve this, in this study, an expert intelligence system named Intelligent System for Predicting Breast Cancer (ISPBC) was developed. The proposed system utilizes an innovative feature selection technique known as Enriched Feature Set (EFS) in order to identify the most appropriate and significant features. The proposed EFS employs the advantages of heuristic search techniques and stochastic hill climbing to select the most significant and important features. The Decision Tree and Random Forest techniques are employed for breast cancer diagnosis, distinguishing between malignant and benign types. The suggested model’s performance was evaluated by comparing measures such as accuracy, precision, and recall through the utilization of tenfold cross-validation. To measure the efficacy of the suggested model, ISPBC’s performance was compared to that of base classifiers and models published in the literature. A maximum accuracy of 96.09% was attained by ISPBC according to the results.

Fine-tuning NER for Triplet Extraction in Medical Knowledge Graph Construction

2025-11-03T11:10:31+07:00

This study presents a new approach for constructing a medical knowledge graph using Named Entity Recognition (NER) to identify entities such as diseases, drugs, or medical procedures, alongside part-of-speech (POS) tagging and dependency parsing to determine words that function as verbs and roots. These extracted words are then used as relations between entities, forming triplets in the format (entity, relation, entity). While the knowledge graph provides a structured representation of medical information, the evaluation primarily reflects the performance of the underlying NLP pipeline (NER, POS tagging, and dependency parsing) used to generate the triplets. Quantitative evaluation was performed using metrics such as precision, recall, and F1-score to assess the accuracy and completeness of entity and relation extraction. The qualitative evaluation involved medical domain experts to assess the relevance and validity of the relationships derived. The results indicate that fine-tuning a pre-trained model for NER and leveraging a pre-trained model for POS tagging and dependency parsing can effectively generate accurate triplets for constructing a medical knowledge graph. This approach demonstrated strong performance, achieving high evaluation scores in both quantitative and qualitative evaluations.

Journal of ICT Research and Applications

An Intelligent System for Predicting Breast Cancer (ISPBC) using a Novel Feature Selection Technique

Fine-tuning NER for Triplet Extraction in Medical Knowledge Graph Construction