Predicting Non-Small Cell Lung Cancer: A Machine Learning Paradigm

Kumar, N. K. Senthil; Vigneswari, D.; Kavya, M; Ramya, K.; Druthi, T. Lakshmi

doi:10.1166/jctn.2018.7406

Cited by 10 publications

(3 citation statements)

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“…The researchers in [31] employed machine learning classifiers to predict hepatitis in their experiment; the naive Bayes classifier outperformed each prediction within an evaluation. For extended and potentially lifethreatening predictions, direct comparisons were made using machine learning algorithms, such as anti-cell tumor growth sequence section non-linear and non-research methods for renal dysfunction [32], biochemical disturbances [33], Optimal process permutation shrubs and trees for type-2 diabetes [34], and fuel cell technology machine studying scheduling technique [35]. [36].…”

Section: IImentioning

confidence: 99%

Using Machine Learning Classifiers, Analyze and Predict Cardiovascular Disease

Vardhan¹,

Kumari²

2022

IJRASET

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A myocardial infarction, indigestion, or even death can take place as a result of several illnesses known as heart disease, including restricted or blocked veins. Depending on the extent of the patient's side effects, the condition is anticipated by the supervised classification classifier. This research intends to investigate how Machine Learning Tree Classifiers depict Heart Disease Prediction. Pattern recognition tree classifiers are analyzed using Random Forest, Decision Tree, Logistic Regression, Support Vector Machine (SVM), and K-nearest Neighbors (KNN) based on their correctness and AUC Gryphon scores. With an execution time of 1.32 seconds, better precision of 85%, and a Coefficient Of determination (r score of 0.8739, the Random Forest machine learning classification surpassed its effectiveness in this investigation of coronary heart disease detection.

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Section: IImentioning

confidence: 99%

Using Machine Learning Classifiers, Analyze and Predict Cardiovascular Disease

Vardhan¹,

Kumari²

2022

IJRASET

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“…Recent work in [9] investigated the DENV serotypes and analyzed the patterns using machine learning classifier for detecting DENV serotypes; the hybrid algorithm MSO-MLP outperformed all classifiers under the analysis. Several works were carried out for investigating infectious disease such as hepatitis [10] and predicting non small cell lung cancer [11] using machine learning classifiers. Hybrid machine learning classifiers [12] can be more effective in classifying patients affected with infectious disease [13].…”

Section: Introductionmentioning

confidence: 99%

Investigating dengue outbreak in Tamil Nadu, India

Kumar

Tulasi²,

Vigneswari³

2020

IJEECS

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<p><span>Dengue has been indigenous to India in last decade. There was a major outbreak in the state of Tamil Nadu in 2017. Here, we investigate the dengue outbreak in parts of Tamil Nadu, India. Dengue case data were obtained from the hospital records in the Chennai district of Tamil Nadu. The data were analyzed using statistical approaches such as correlation and regression. The result shows that the dengue outbreak in Tamil Nadu during 2017 was due to the population, water stagnation, and sewage, whereas the human activity weren’t the cause of the dengue outbreak which caused 65 deaths. Male constitutes 54.71% whereas female accounted for 45.29% of dengue incidence in Tamil Nadu, majority deaths were children aged less than 10 years due to the outbreak of Dengue Hemorrhagic Fever (DHF). This investigation was evaluated using mathematical regressions, Geographically Weighted Regression (GWR) regression outperformed Ordinary Least Square (OLS) regression model in detecting dengue incidence. This investigation can be strengthened by implementing a surveillance system in parts of Tamil Nadu before an outbreak.</span></p>

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“…La predicción de cáncer de pulmón no microcítico es presentado en [53]. Los autores comparan el desempeño de algoritmos de ML para la predicción de este tipo de cáncer, utilizando información sobre marcadores genéticos de los pacientes para conocer si es posible que un modelo de ML tenga una precisión aceptable para la predicción de este tipo de cáncer.…”

Section: Datos Estructuradosunclassified

Modelos de aprendizaje automático para el apoyo en la clasificación de tipos de cáncer a partir de datos estructurados y no estructurados de expedientes clínicos

Montelongo-González¹

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The existence of large volumes of data generated by the health area presents an important opportunity for analysis. This can obtain information to support physicians in the decisionmaking process for the diagnosis or treatment of diseases, such as cancer. The present work shows a methodology for the classification of patients with liver, lung and breast cancer, through machine learning models, to obtain the model that performs best in the classification. The methodology considers three classification models: Support Vector Machines (SVM), Multi-Layer Perceptron (MLP) and AdaBoost using both structured and unstructured information from the patient's clinical records. Results show that the best classification model is MLP using only unstructured data, obtaining 89% of precision, showing the usefulness of this type of data in the classification of cancer patients.

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Predicting Non-Small Cell Lung Cancer: A Machine Learning Paradigm

Cited by 10 publications

References 0 publications

Using Machine Learning Classifiers, Analyze and Predict Cardiovascular Disease

Using Machine Learning Classifiers, Analyze and Predict Cardiovascular Disease

Investigating dengue outbreak in Tamil Nadu, India

Modelos de aprendizaje automático para el apoyo en la clasificación de tipos de cáncer a partir de datos estructurados y no estructurados de expedientes clínicos

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