Abstract
Artificial intelligence provides modelling on machines by simulating the human brain using learning and decision-making abilities. Early diagnosis is highly effective in reducing mortality in cancer. This study aimed to combine cancer-associated risk factors including genetic variations and design an artificial intelligence system for risk assessment. Data from a total of 268 breast cancer patients have been analysed for 16 different risk factors including genetic variant classifications. In total, 61 BRCA1, 128 BRCA2 and 11 both BRCA1 and BRCA2 genes associated breast cancer patients’ data were used to train the system using Mamdani’s Fuzzy Inference Method and Feed-Forward Neural Network Method as the model softwares on MATLAB. Sixteen different tests were performed on twelve different subjects who had not been introduced to the system before. The rates for neural network were 99.9% for training success, 99.6% for validation success and 99.7% for test success. Despite neural network’s overall success was slightly higher than fuzzy logic accuracy, the results from developed systems were similar (99.9% and 95.5%, respectively). The developed models make predictions from a wider perspective using more risk factors including genetic variation data compared with similar studies in the literature. Overall, this artificial intelligence models present promising results for BRCA variations’ risk assessment in breast cancers as well as a unique tool for personalized medicine software.
Highlights
Diagnosis is the initial step in medical practice [1]
Each risk factor was divided into sub-groups known as membership functions
It is thought that the use of the accumulated data in artificial intelligence applications would determine risk score assessment for the breast cancer which is the most common in women
Summary
Diagnosis is the initial step in medical practice [1]. The integration of artificial intelligence (AI) approaches such as machine learning including fuzzy logic, neural network 4.0/).can transform big data into clinically actionable knowledge [2] and will become the foundation of precision medicine in three ways: quick decision making for clinicians, reasonable source for healthcare systems and individual data for better and precise treatment [3]. AI has been continuing to improve characterizations in genetic and molecular medicine since it was first conceived by providing knowledge management [4]. This has given rise to evidence-based computerized diagnostic tools, intended to aid physicians in making primary medical decisions and early diagnosis, which helps reduce the treatment options and increase survival rate [5]. Some of the most important areas in medical research are related to cancer and cardiovascular diseases [4,6,7] It is based on the complex clinical decision-making method that often accompanies the degree of uncertainty [8]
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