Interpret Your Care: Predicting the Evolution of Symptoms for Cancer Patients

• URL: http://arxiv.org/abs/2302.09659v1
• Date: Sun, 19 Feb 2023 19:29:59 GMT
• Title: Interpret Your Care: Predicting the Evolution of Symptoms for Cancer Patients
• Authors: Rupali Bhati, Jennifer Jones, Audrey Durand
• Abstract summary: We implement an interpretable decision tree based model called LightGBM on real-world patient data consisting of 20163 patients. Our empirical results show that the value of the previous level of a symptom is a key indicator for prediction.
• Score: 5.175050215292647
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Cancer treatment is an arduous process for patients and causes many side-effects during and post-treatment. The treatment can affect almost all body systems and result in pain, fatigue, sleep disturbances, cognitive impairments, etc. These conditions are often under-diagnosed or under-treated. In this paper, we use patient data to predict the evolution of their symptoms such that treatment-related impairments can be prevented or effects meaningfully ameliorated. The focus of this study is on predicting the pain and tiredness level of a patient post their diagnosis. We implement an interpretable decision tree based model called LightGBM on real-world patient data consisting of 20163 patients. There exists a class imbalance problem in the dataset which we resolve using the oversampling technique of SMOTE. Our empirical results show that the value of the previous level of a symptom is a key indicator for prediction and the weighted average deviation in prediction of pain level is 3.52 and of tiredness level is 2.27.

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