Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Machine Learning for Green Smart Health Toward Improving Cancer Data Feature Awareness
Radiation therapy and chemotherapy may be considered life-saving treatments for cancer patients. Though this treatment is not always successful, it left very higher effects on our environment. Drugs used in cancer treatment stop the growth and division of cells; released into the environment, they can affect the ecosystem through altered fertility and increased genetic defects. For a safer greener world undoubtedly, we can rely on machine learning technology to diagnose cancer in early stages. Hence, we may call that choosing the right influencing feature may reduce morbidity and green technology for early cancer diagnosis. Cervical cancer is an excellent example for such a study, as well as impacting individuals, families, and the environment. Cervical cancer presents almost no symptoms at the early stages of development of this condition. Because multi-factors may be involved, this demands a lot of research and analysis to identify causative or linked features. Choosing the right influencing feature is a challenging field in data science due to the presence and complexity of multi-dimensional data. The researchers have applied and optimized an ensemble learning algorithm as it is the best model for multi-modal medical data when relatively high dimensionality is present. The main objective of this study was to minimize the dependency on data pre-processing techniques, while analyzing the data (filling/ignoring missing values with the statistical method). Also, increasing such awareness of feature selection will immensely impact the environment (e.g., chemotherapy-free, less radiation therapy). Main factors were studied and validated using root mean square error (RSME) and mean absolute error (MAE). The classification accuracy for features was obtained by tenfold cross-validation and test (where 66% is training data and 34% test data).
Machine Learning for Green Smart Health Toward Improving Cancer Data Feature Awareness
Radiation therapy and chemotherapy may be considered life-saving treatments for cancer patients. Though this treatment is not always successful, it left very higher effects on our environment. Drugs used in cancer treatment stop the growth and division of cells; released into the environment, they can affect the ecosystem through altered fertility and increased genetic defects. For a safer greener world undoubtedly, we can rely on machine learning technology to diagnose cancer in early stages. Hence, we may call that choosing the right influencing feature may reduce morbidity and green technology for early cancer diagnosis. Cervical cancer is an excellent example for such a study, as well as impacting individuals, families, and the environment. Cervical cancer presents almost no symptoms at the early stages of development of this condition. Because multi-factors may be involved, this demands a lot of research and analysis to identify causative or linked features. Choosing the right influencing feature is a challenging field in data science due to the presence and complexity of multi-dimensional data. The researchers have applied and optimized an ensemble learning algorithm as it is the best model for multi-modal medical data when relatively high dimensionality is present. The main objective of this study was to minimize the dependency on data pre-processing techniques, while analyzing the data (filling/ignoring missing values with the statistical method). Also, increasing such awareness of feature selection will immensely impact the environment (e.g., chemotherapy-free, less radiation therapy). Main factors were studied and validated using root mean square error (RSME) and mean absolute error (MAE). The classification accuracy for features was obtained by tenfold cross-validation and test (where 66% is training data and 34% test data).
Machine Learning for Green Smart Health Toward Improving Cancer Data Feature Awareness
Green Energy,Technology
Lahby, Mohamed (Herausgeber:in) / Al-Fuqaha, Ala (Herausgeber:in) / Maleh, Yassine (Herausgeber:in) / Hasan, Md Rajib (Autor:in) / Alani, Noor H. S. (Autor:in) / Hasan, Rashedul (Autor:in)
22.04.2022
22 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
Feature selection , Ensemble learning , Chemotherapy-free , Green smart education , Machine learning , WEKA , MATLAB , Cervical cancer , Green smart health Environment , Sustainable Development , Power Electronics, Electrical Machines and Networks , Cities, Countries, Regions , Renewable and Green Energy , Communications Engineering, Networks , Energy
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