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

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

breast cancer; enriched feature set; heuristic search techniques; intelligent system; random forest; stochastic hill climbing

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.

Akhil Kumar Das, Saroj Kr. Biswas, Ardhendu Mandal, Arijit Bhattacharya & Debasmita Saha

DOI : https://doi.org/10.5614/itbj.ict.res.appl.2025.19.2.2

IRCS-ITB

24 November 2025

Sri Wahyuni

ISSN : 2337-5787

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English

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