Applying Machine Learning of Erythrocytes Dynamic Antigens Store in Medicine

Rafea, Mahmoud; Elkafrawy, Passent; Nasef, Mohammed M.; Elnemr, Rasha; Jamal, Amani

doi:10.3389/fmolb.2019.00019

Cited by 2 publications

(2 citation statements)

References 24 publications

(29 reference statements)

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“…Phase one: the generation of EDAS data, our data is based on the previous experiment by Rafea et al (2019), where the total generated cases are 100K record. Malignant tumor patients are 41,742 records from the total 100 K, which was concluded from the experiment by Rafea et al (2019). Malignant Tumor (Mi) has 20 types (M1, M2, .…”

Section: Experiments and Resultsmentioning

confidence: 99%

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A Theoretical Approach for Correlating Proteins to Malignant Diseases

Elnemr¹,

Nasef

Elkafrawy

et al. 2020

Front. Mol. Biosci.

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These biological factors induce changes in the body and consequently, they lead to Malignant Tumors. Some habits and behaviors initiate these biological factors. In effect, the immune system cannot recognize a Malignant Tumor as foreign tissue. In order to discover a fascinating pattern of these habits, behaviors, and diseases and to make effective decisions, different machine learning techniques should be used. This research attempts to find the association between normal proteins (environmental factors) and diseases that are difficult to diagnose and propose justifications for those diseases. This paper proposes a technique for medical data mining using association rules. The proposed technique overcomes some of the limitations in current association algorithms such as the Apriori algorithm and the Equivalence CLAss Transformation (ECLAT) algorithm. A modification to the Apriori algorithm has been proposed to mine Erythrocytes Dynamic Antigens Store (EDAS) data in a more efficient and tractable way. The experiments inferred that there is a relation between normal proteins as environment proteins, food proteins, commensal proteins, tissue proteins, and disease proteins. Also, the experiments show that habits and behaviors are associated with certain diseases. The presented tool can be used in clinical laboratories to discover the biological causes of malignant diseases.

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Section: Experiments and Resultsmentioning

confidence: 99%

“…To maximize the utility of the EDAS, computer knowledge processing capability was adopted to increase the profit of this discovery. To this endeavor, a random generation of the EDAS model was described in Rafea et al (2019). This random generation was based on a mathematical model that simulates reality.…”

Section: Introductionmentioning

confidence: 99%