Volume : VIII, Issue : XII, December - 2019
Explanatory Artificial Intelligence (XAI) in the prediction of post-operative life expectancy in lung cancer patients
Manu Siddhartha, Paramita Maity, Rajendra Nath
Abstract :
In this paper, we present the application of bagging based ensemble technique i.e., Random Forest to predict survival status of lung cancer patients who underwent major lung resections for primary lung cancer using thoracic surgery. Further we interpret the random forest ensemble machine learning model to find out the reasons for particular prediction also known as explanatory artificial intelligence (XAI).The data used for this paper is thoracic surgery patient’s data, in which data was collected retrospectively at Wroclaw Thoracic Surgery Centre in the year 2007 and 2011. As it is highly imbalanced data so to prevent machine learning classifier to predict biased decision in favor of majority class we used the synthetic minority oversampling technique (SMOTE) to deal with it. The proposed solution combines the benefits of using bagging based ensemble method with identification and interpretation of risk factors in classifying survival status of lung cancer patients which can help assist the medical practitioner in their diagnosis
Keywords :
Thoracic Surgery Explanatory Artificial Intelligence Imbalanced data Interpretable Machine Learning Healthcare Analytics
Article:
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DOI : 10.36106/ijsr
Cite This Article:
EXPLANATORY ARTIFICIAL INTELLIGENCE (XAI) IN THE PREDICTION OF POST-OPERATIVE LIFE EXPECTANCY IN LUNG CANCER PATIENTS, Manu Siddhartha, Paramita Maity, Rajendra Nath INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH : Volume-8 | Issue-12 | December-2019
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EXPLANATORY ARTIFICIAL INTELLIGENCE (XAI) IN THE PREDICTION OF POST-OPERATIVE LIFE EXPECTANCY IN LUNG CANCER PATIENTS, Manu Siddhartha, Paramita Maity, Rajendra Nath INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH : Volume-8 | Issue-12 | December-2019
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