A Novel Genetic Operator for Genetic Folding Algorithm: A Refolding Operator and a New Genotype

Mezher, Mohammad A.; Abbod, Maysam F.

doi:10.5815/ijem.2017.06.01

Cited by 1 publication

(1 citation statement)

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“…Each GF kernel chromosome was divided into a head segment that only carries functions and a tail segment containing terminals. However, the size of the head segment must be determined ahead of time, but the size of the tails segment does not need to be determined since the GF algorithm predicts the number of genes needed based on the pairs required for the drawn functions ( Mezher & Abbod, 2017 ). Furthermore, the GF algorithm predicts the number of operands (terminals) necessary each time the GF algorithm generates different operators (function) at random.…”

Section: Introductionmentioning

confidence: 99%

An enhanced Genetic Folding algorithm for prostate and breast cancer detection

Mezher

Altamimi

2022

PeerJ Computer Science

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Cancer’s genomic complexity is gradually increasing as we learn more about it. Genomic classification of various cancers is crucial in providing oncologists with vital information for targeted therapy. Thus, it becomes more pertinent to address issues of patient genomic classification. Prostate cancer is a cancer subtype that exhibits extreme heterogeneity. Prostate cancer contributes to 7.3% of new cancer cases worldwide, with a high prevalence in males. Breast cancer is the most common type of cancer in women and the second most significant cause of death from cancer in women. Breast cancer is caused by abnormal cell growth in the breast tissue, generally referred to as a tumour. Tumours are not synonymous with cancer; they can be benign (noncancerous), pre-malignant (pre-cancerous), or malignant (cancerous). Fine-needle aspiration (FNA) tests are used to biopsy the breast to diagnose breast cancer. Artificial Intelligence (AI) and machine learning (ML) models are used to diagnose with varying accuracy. In light of this, we used the Genetic Folding (GF) algorithm to predict prostate cancer status in a given dataset. An accuracy of 96% was obtained, thus being the current highest accuracy in prostate cancer diagnosis. The model was also used in breast cancer classification with a proposed pipeline that used exploratory data analysis (EDA), label encoding, feature standardization, feature decomposition, log transformation, detect and remove the outliers with Z-score, and the BAGGINGSVM approach attained a 95.96% accuracy. The accuracy of this model was then assessed using the rate of change of PSA, age, BMI, and filtration by race. We discovered that integrating the rate of change of PSA and age in our model raised the model’s area under the curve (AUC) by 6.8%, whereas BMI and race had no effect. As for breast cancer classification, no features were removed.

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Section: Introductionmentioning

confidence: 99%