Machine Learning Semi-Supervised Algorithms for Gene Selection: A Review

Zeebaree, Diyar Qader; Hasan, Dathar Abas; Abdulazeez, Adnan Mohsin; Ahmed, Falah Y. H.; Hasan, Ramadan T.

doi:10.1109/icset53708.2021.9612526

Cited by 8 publications

(2 citation statements)

References 43 publications

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“…ML is the study of computational algorithms that are enhanced from experience automatically. It is considered as an artificial intelligence subset [1,2]. Orderly to produce foretelling or decision without being specifically programmed to do so, ML algorithms create a model population based on a sample, defined as 'training data' [3,4].…”

Section: Introductionmentioning

confidence: 99%

Classification and Regression Based on Decision Tree Algorithm for Machine Learning

Sivananda,

Kumar

2024

IJSREM

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Decision tree classifiers are regarded to be a standout of the most well-known methods to data classification representation of classifiers. Different researchers from various fields and backgrounds have considered the problem of extending a decision tree from available data, such as machine study, pattern recognition, and statistics. In various fields such as medical disease analysis, text classification, user smartphone classification, images, and many more the employment of Decision tree classifiers has been proposed in many ways. This paper provides a detailed approach to the decision trees. Furthermore, paper specifics, such as algorithms/approaches used, datasets, and outcomes achieved, are evaluated and outlined comprehensively. In addition, all of the approaches analyzed were discussed to illustrate the themes of the authors and identify the most accurate classifiers. As a result, the uses of different types of datasets are discussed and their findings are analyzed. Keywords: Machine Learning, Supervised, Classification, Regression, Decision Tree.

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Section: Introductionmentioning

confidence: 99%

Classification and Regression Based on Decision Tree Algorithm for Machine Learning

Sivananda,

Kumar

2024

IJSREM

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“…Many systems have insufficient detection accuracy, and particular systems must be constructed to attain the highest level of precision. Machine learning and image-processing approaches were used to detect and classify lung cancer (Abdulqader et al, 2020). However, some characteristics of lung cancer patients, such as smoking behaviors, may contribute to the early diagnosis of the illness.…”

Section: Introductionmentioning

confidence: 99%

Prediction Lung Cancer Based Critical Factors Using Machine Learning

Murad,

Awlla,

Moahmmed

2023

SJUOZ

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Many people around the world have lung cancer. Lung cancer has a poor prognosis and a high mortality rate. Through image recognition and data analytics, computers can play a significant role in detecting various types of cancer disease. This paper provides an effective method to predict lung cancer in an early stage with high accuracy ratio. This research proposed data analytics to determine the accuracy ratio of lung cancer patients using supervised machine learning algorithms (Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF). The dataset for this study was obtained from "Data World," which contains 1,000 diseases. Machine learning algorithms enable us to identify lung cancer risk factors, which aid in diagnosing lung cancer. This study shows that those algorithms can classify lung cancer patients, with Random Forest having the highest accuracy of 98.507%.

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Machine Learning and Deep Learning

Jeyachitra,

Manochandar

2023

Multimodal Biometric and Machine Learning Technologies

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Machine Learning Semi-Supervised Algorithms for Gene Selection: A Review

Cited by 8 publications

References 43 publications

Classification and Regression Based on Decision Tree Algorithm for Machine Learning

Classification and Regression Based on Decision Tree Algorithm for Machine Learning

Prediction Lung Cancer Based Critical Factors Using Machine Learning

Machine Learning and Deep Learning

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