Application of Decision-Tree-Based Machine Learning Algorithms for Prediction of Antimicrobial Resistance

Yasir, Muhammad; Karim, Asad Mustafa; Malik, Sumera Kausar; Bajaffer, Amal A.; Azhar, Esam I.

doi:10.3390/antibiotics11111593

Cited by 17 publications

(20 citation statements)

References 18 publications

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“…The application of a metaclassifier proved to be the most effective model for identifying blackcurrant powders. The ensembles of classifiers extracting single classifiers, i.e., random forest, decision tree, or bagging have become a better solution than the existing neural modeling tools [ 47 , 55 , 58 , 63 , 72 ]. But the important thing is that the choice of method depends on the specific task at hand and the number of cases in the learning set.…”

Section: Resultsmentioning

confidence: 99%

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Efficiency of Identification of Blackcurrant Powders Using Classifier Ensembles

Przybył,

Walkowiak,

Kowalczewski

2024

Foods

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In the modern times of technological development, it is important to select adequate methods to support various food and industrial problems, including innovative techniques with the help of artificial intelligence (AI). Effective analysis and the speed of algorithm implementation are key points in assessing the quality of food products. Non-invasive solutions are being sought to achieve high accuracy in the classification and evaluation of various food products. This paper presents various machine learning algorithm architectures to evaluate the efficiency of identifying blackcurrant powders (i.e., blackcurrant concentrate with a density of 67 °Brix and a color coefficient of 2.352 (E520/E420) in combination with the selected carrier) based on information encoded in microscopic images acquired via scanning electron microscopy (SEM). Recognition of blackcurrant powders was performed using texture feature extraction from images aided by the gray-level co-occurrence matrix (GLCM). It was evaluated for quality using individual single classifiers and a metaclassifier based on metrics such as accuracy, precision, recall, and F1-score. The research showed that the metaclassifier, as well as a single random forest (RF) classifier most effectively identified blackcurrant powders based on image texture features. This indicates that ensembles of classifiers in machine learning is an alternative approach to demonstrate better performance than the existing traditional solutions with single neural models. In the future, such solutions could be an important tool to support the assessment of the quality of food products in real time. Moreover, ensembles of classifiers can be used for faster analysis to determine the selection of an adequate machine learning algorithm for a given problem.

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Section: Resultsmentioning

confidence: 99%

“…It can be a very flexible yet effective model, especially in the classification aspect. Another algorithm is RidgeClassifier based on ridge regression [ 58 ]. This model was characterized by a high need for data control in optimizing the results, among other things.…”

Section: Methodsmentioning

confidence: 99%

Efficiency of Identification of Blackcurrant Powders Using Classifier Ensembles

Przybył,

Walkowiak,

Kowalczewski

2024

Foods

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“…Therefore, it is possible to understand the features or interaction of features responsible for a particular decision. Gradient-boosting models—ensemble methods from decision tree—have been successfully used in AMR prediction [ 28 , 72 , 73 ]. Table 1 shows different DL/ML models used in AMR identification and applications.…”

Section: Artificial Intelligence (Dl/ml) For Antimicrobial Resistancementioning

confidence: 99%

Artificial Intelligence for Antimicrobial Resistance Prediction: Challenges and Opportunities towards Practical Implementation

2023

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Antimicrobial resistance (AMR) is emerging as a potential threat to many lives worldwide. It is very important to understand and apply effective strategies to counter the impact of AMR and its mutation from a medical treatment point of view. The intersection of artificial intelligence (AI), especially deep learning/machine learning, has led to a new direction in antimicrobial identification. Furthermore, presently, the availability of huge amounts of data from multiple sources has made it more effective to use these artificial intelligence techniques to identify interesting insights into AMR genes such as new genes, mutations, drug identification, conditions favorable to spread, and so on. Therefore, this paper presents a review of state-of-the-art challenges and opportunities. These include interesting input features posing challenges in use, state-of-the-art deep-learning/machine-learning models for robustness and high accuracy, challenges, and prospects to apply these techniques for practical purposes. The paper concludes with the encouragement to apply AI to the AMR sector with the intention of practical diagnosis and treatment, since presently most studies are at early stages with minimal application in the practice of diagnosis and treatment of disease.

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“…A decision tree might be used to identify asthma from a patient's wheezing, coughing, and shortness of breath (Tyagi & Singh, 2014). Based on clinical data, decision trees can be used to choose the best course of treatment for a patient (Yasir et al, 2022). The optimal course of action for a patient with heart disease, for instance, can be decided using a decision tree depending on the condition of the patient, age, blood pressure, lipid profile, and other alarming factors.…”

Section: Applications Of Deep and Machine Learning In Medical Fieldsmentioning

confidence: 99%

Advancements in medical diagnosis and treatment through machine learning: A review

Ahsan,

Khan,

Khan

et al. 2023

Expert Systems

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The aptness of machine learning (ML) to learn from large datasets, discover trends, and make predictions has demonstrated its potential to metamorphose the medical field. Medical data analysis with ML algorithms can improve patient outcomes in terms of both treatment and diagnosis. This paper investigates the numerous possibilities of ML in the medical industries, including radiology, pathology, genomics, and clinical decision‐making. It also goes over the benefits and drawbacks of ML in various sectors as well as the limitations that come with its application. It illustrates the potential advantages of ML, such as better accuracy and efficiency in diagnosis and individualized treatment programs, through a review of previous studies. Lastly, it provides perspectives on prospective advancements and prospects for the discipline. This study also intends to investigate the applications of deep learning (DL) in the medical field. DL algorithms have performed exceptionally satisfactorily in several healthcare‐related fields. The main conclusions of the study are summarized, and their ramifications for the healthcare sector are discussed in this paper's conclusion. This paper intends to contribute to a greater understanding of the prevailing state of the discipline and the possibility for future developments by emphasizing the prospects of these methodologies to alter medical study and clinical practice.

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Application of Decision-Tree-Based Machine Learning Algorithms for Prediction of Antimicrobial Resistance

Cited by 17 publications

References 18 publications

Efficiency of Identification of Blackcurrant Powders Using Classifier Ensembles

Efficiency of Identification of Blackcurrant Powders Using Classifier Ensembles

Artificial Intelligence for Antimicrobial Resistance Prediction: Challenges and Opportunities towards Practical Implementation

Advancements in medical diagnosis and treatment through machine learning: A review

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