Designing Interpretable and Transparent Machine Learning Models for Early Detection of Breast Cancer - Souvik Santra; Arindam Sarkar; Pradip Sahoo; Trishita Ghosh; Siddharth Gautam; Arunava Bhowmik; Soumya Majumdar; Abhishek Bhowmik; Sonali Bhowmik; Paramita Sarkar (International Journal of Computer Sciences and Engineering, 2023)
Annually, over 1.15 million individuals are diagnosed with breast cancer across the globe. Today, only a select few accurate prognostic and predictive indicators are utilized in the clinical management of these patients. Early recognition of this deadly disease is pivotal, as it can reduce mortality rates while enhancing the life expectancy of those affected by breast cancer. Women are severely impacted by this condition, which has a high incidence and lethality rate. The absence of sturdy prognosis models complicates the medical professional`s ability to develop a treatment strategy that could extend the patient`s lifespan. Consequently, the need of the hour is to devise techniques that minimize error to boost accuracy. This study contrasts four algorithms, that predict the outcome of breast cancer, utilizing diverse datasets. All trials are performed within a simulated environment and facilitated on the JUPYTER platform. The research objective is divided into three sectors. The first sector entails predicting cancer prior to diagnosis, the second involves forecasting diagnosis and therapy, and the third centres on outcome during treatment. The proposed initiative can be employed to predict the outcomes of various methods, with the appropriate techniques chosen based on necessity. This investigation is undertaken to predict accuracy. Future studies can focus on predicting other distinct parameters, and breast cancer research can be classified based on these other parameters.