Quantum-Inspired Computing for Breast Cancer Classification
Abstract
Patients at an early stage, which can help with prompt diagnosis and treatment of the condition. This study utilizes a retrospective cohort design, where health records of patients with and without breast cancer were obtained from a large healthcare database. The data were pre- processed to remove missing values, and feature engineering techniques were used to extract relevant features. The predictive models were created, QSVM, Classical ML models (SVM, Random Forest. The goal of this research was to develop a predictive model for detecting breast cancer early in patients. The models were able to achieve high accuracy, precision, and recall rates, indicating their effectiveness in predicting the likelihood of breast cancer in patients. The model can be used to pinpoint people at high risk for illness development who need additional diagnostic tests or early intervention. The creation of a Breast cancer predictive model using health information is the sole focus of this work. The model can be incorporated into clinical decision support systems. In order to examine the viability of adopting the model in clinical practice, more research is required to validate the model in a variety of demographics and situations.
How strong is the evidence?
Evidence Hub reads this as a cohort study, done in people. Its evidence level is 3 of 5 (Moderate · trial / cohort / secondary analysis). Trials without a large randomized design, cohorts and secondary analyses give moderate evidence.
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