Drug Administration Route Classification using Machine Learning Models

Shobana, G.; Bushra, S. Nikkath

doi:10.1109/iciss49785.2020.9315975

Cited by 4 publications

(4 citation statements)

References 8 publications

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“…The second round of testing would involve the medication administration route, even though its disease-inhibiting properties are identified in the first stage. At this stage, the method of administration, such as Oral, Parenteral, and Topical, is identified [21].…”

Section: Literature Surveymentioning

confidence: 99%

Leukocytes Classification Using Convolutional Neural Network

Bushra¹,

Sheeba²,

Subasini³

et al. 2023

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Maintaining a healthy life is very important in today’s world. Our body has ability to fight against contagious diseases with the aid of white blood corpuscles (WBC) which generates natural immune system. Preserving a good WBC count is crucial as it leads to various hematological problems, one among them is Leukemia, a condition which results in blood cancer, huge accumulation of WBC cells in bone marrow, an abnormal growth of WBC cells, hinders natural immune system fighting against infectious diseases. White blood cells can be categorised into Eosinophils, Lymphocytes, Monocytes, Neutrophils and Basophils. In this study, a pre-trained Convolutional neural network architecture called Le Net is used to automatically classify WBC cells. It efficiently classifies WBC cells from the given input sample blood cell images compared to other pre trained models such as Alex-Net and custom-built CNN called white capsule net. The hyper parameters Le Net are fine tuned to yield higher accuracy of 96%.

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Section: Literature Surveymentioning

confidence: 99%

Leukocytes Classification Using Convolutional Neural Network

Bushra¹,

Sheeba²,

Subasini³

et al. 2023

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“…In the meantime, with the introducing of computer science by using AI, it brings more opportunities for new drugs' industry. G. Shobana and Dr. S. Nikkath Bushra trained the Machine Learning models with observations that define the ADMET properties, pharmacokinetics and physicochemical properties of drugs [1].…”

Section: Introductionmentioning

confidence: 99%

“…It is generally classified as three major types. The rate of absorption of the drug varies depending on the routes [1].…”

Section: Introductionmentioning

confidence: 99%

Research on Drug Classification Using Machine Learning Model

Chen

2024

HSET

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In the recent years, more and more fields around the development and application of artificial intelligence have received new improvements and breakthroughs. Among them, drug design and classification are one of the most popular application areas which can provide the greatest help to the society. According to statistics, the number of newly listed drugs in the world is decreasing year by year. At the same time, the risks and costs of drug development are increasing year by year which illustrates a different trend compared with the newly listed drugs. In this case, the application of artificial intelligence technology provides a new idea and opportunity to solve the problem of drug design and classification with higher accuracy than human work. This paper aims to do a research based on using machine learning models to produce suitable outcomes for patients of different drug types in order to reduce the working pressure of doctors in the hospitals.

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“…Various machine learning methods, namely Artificial Neural Network (ANN), Decision Tree (DT), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Random Forest (RF) can be used to handle classification tasks. Several previous studies have successfully used these methods in classifying drug administration routes, with [4] achieving a 97% accuracy rate via Random Forest using drug compound data retrieved from ChEMBL. Similarly, [5] classified drugs that cause QT syndrome by investigating ECG reports using SVM and KNN, resulting in 89% accuracy using ECG data from Physionet.…”

Section: Introductionmentioning

confidence: 99%

Comparison of the Accuracy of Drug User Classification Models Using Machine Learning Methods

Basuni,

Amril Mutoi Siregar

2023

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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Cases of drug abuse are on the rise, with many users entering the addiction phase, often resulting in overdose and death. Drugs are chemical compounds that are capable of affecting biological functions, can induce feelings of happiness and reduce pain. To address this growing problem, a proactive measure is needed. Therefore, this study aims to classify drug users and non-users, so that health workers and therapists can educate about the dangers of drugs to non-users and rehabilitate drug users. This study uses drug consumption data taken from the UCI Irvine Machine Learning Repository. The data consists of 1885 rows with 32 attributes and 2 classes, where there are 18 types of legal and illegal drugs. This research utilizes machine learning methods, specifically Artificial Neural Network (ANN), Decision Tree (DT), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Random Forest (RF), in addition to evaluation methods such as Confusion Matrix and Area Under Curve (AUC). The results showed that RF outperformed the other methods, with accuracy, precision, and recall of 93%, and an f1-score of 89%, while the AUC value was still suboptimal at 0.66. DT had the worst results, with 82% accuracy, 87% precision, 82% recall, 84% f1-score, and an AUC value of 0.56. With these results, this research can be continued into an application that can classify drug users and non-users.

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Drug Administration Route Classification using Machine Learning Models

Cited by 4 publications

References 8 publications

Leukocytes Classification Using Convolutional Neural Network

Leukocytes Classification Using Convolutional Neural Network

Research on Drug Classification Using Machine Learning Model

Comparison of the Accuracy of Drug User Classification Models Using Machine Learning Methods

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