A Recruitment Big Data Approach to interplay of the Target Drugs

Alzyadat, Wael; Muhairat, Mohammad I.; Alhroob, Aysh; Rawashdeh, Thamer

doi:10.15849/zujijasaca.220328.01

Cited by 4 publications

(8 citation statements)

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“…In [2], Alzyadat et al proposed an approach to predict a targeted drug for the variety of large data structures measured by a stability scale in the preprocessing phase. Their approach performs quality data analysis using correlation methods to identify feature choices related to mapping data, which concerns the basic methods for predicting data based on the Kmean cluster and decision tree.…”

Section: Related Workmentioning

confidence: 99%

“…The availability of huge amounts of data has increased the need for data mining techniques to produce useful knowledge [1]. Accurate analyses of medical drug data are required to discover appropriate alternative medication for a patient, which is gaining with increasing data in the health care and biomedical communities [2] [7].…”

Section: Introductionmentioning

confidence: 99%

“…Raw drugs data requires a clear description and interpretation for analysis purposes, this is to find the similarities between drugs that have the same properties and then to find the alternative drugs [2]. The drugs data has many different attributes collected from different sources.…”

Section: Introductionmentioning

confidence: 99%

“…It also helps people who are looking for a drug with a lower price and cannot afford to purchase the original drug. So, the proposed model can help them to identify other drugs alternatives that are more affordable to them at the same time, and such options have similar chemical properties to the original option [2]. This research aims to predict alternative medical drug using AI algorithms.…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, using AI to predict drug efficacy and safety involves analyzing data from various sources, such as preclinical trial results, electronic health records, and genetic data, to identify patterns that may indicate a drug's effectiveness or potential side effects. This can be done more quickly and efficiently than traditional methods and can help reduce the cost and time associated with drug development [1] [2]. In summary, drug discovery is the process of identifying and developing new drugs from scratch, while using AI to predict drug efficacy and safety is a way to analyze existing data and make predictions about how a drug will perform in preclinical and clinical trials.…”

Section: Introductionmentioning

confidence: 99%

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Predicting Quality Medical Drug Data Towards Meaningful Data using Machine Learning

Al-Showarah,

Al-Taie,

Salman

et al. 2023

IJACSA

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This research aims to improve the process of finding alternative drugs by utilizing artificial intelligence algorithms. It is not an easy task for human beings to classify the drugs manually, as this requires much longer time and more effort than doing it using classifiers. The study focuses on predicting high-quality medical drug data by considering ingredients, dosage forms, and strengths as features. Two datasets were generated from the original drug dataset, and four machine learning classifiers were applied to these datasets: Random Forest, Support Vector Machine, Naive Bayes, and Decision Tree. The classification performance was evaluated under three different scenarios, which varied the ratio of the training and test data for both datasets, as follows: (i) 80% (training) and 20% (test dataset), (ii) 70% (training) and 30% (test dataset), and (iii) 50% (training) and 50% (test dataset). The results indicated that the Decision Tree, Naive Bayes, and Random Forest classifiers showed superior performance in terms of classification accuracy, with over 90% accuracy achieved in all scenarios. The results also showed that there was no significant difference between the results of the two datasets. The findings of this study have implications for streamlining the process of identifying alternative drugs.

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Section: Related Workmentioning

confidence: 99%