The application of machine learning techniques in prediction of quality of life features for cancer patients

Abstract

Quality of life (QoL) is one of the major issues for cancer patients. With the advent of medical databases containing large amounts of relevant QoL information it becomes possible to train predictive QoL models by machine learning (ML) techniques. However, the training of predictive QoL models poses several challenges mostly due to data privacy concerns and missing values in patient data. In this paper, we analyze several classification and regression ML models predicting QoL indicators for breast and prostate cancer patients. Three different approaches are employed for imputing missing values, and several settings for data privacy preserving are tested. The examined ML models are trained on datasets formed from two databases containing a large number of anonymized medical records of cancer patients from Sweden. Two learning scenarios are considered: centralized and federated learning. In the centralized learning scenario all patient data coming from different data sources is collected at a central location prior to model training. On the other hand, federated learning enables collective training of machine learning models without data sharing. The results of our experimental evaluation show that the predictive power of federated models is comparable to that of centrally trained models for short-term QoL predictions, whereas for long-term periods centralized models provide more accurate QoL predictions. Furthermore, we provide insights into the quality of data preprocessing tasks (missing value imputation and differential privacy).

Personalized Plasma Medicine for Cancer: Transforming Treatment Strategies with Mathematical Modeling and Machine Learning Approaches

“…Treatment selection [102], efficacy [103][104][105], response assessment [106][107][108][109][110], and outcome prediction [111][112][113][114][115] have also seen remarkable enhancements. These advancements have paved the way for highly personalized cancer treatments, offering a ray of hope within the continually evolving healthcare landscape [116][117][118]. Although ML has been applied in various treatment approaches in cancer medicine, its use in plasma medicine is relatively limited (Figure 2).…”

Personalized Plasma Medicine for Cancer: Transforming Treatment Strategies with Mathematical Modeling and AI Algorithms

“…Evolution of AI with medicine, particularly with the integration of ML has opened new possibilities in all aspects of oncology, from improving diagnostics to personalized cancer treatment and improved patient care in the ever-evolving health care landscape [69][70][71][72][73][74][75][76][77][78]. Although ML has been applied in various treatment approaches in cancer medicine, its use in plasma medicine is relatively limited (Figure 2).…”

Personalized Plasma Medicine for Cancer: Transforming Treatment Strategies with Mathematical Modeling and AI Algorithms

“…Treatment selection [99], efficacy [100][101][102] and response assessment [103][104][105][106][107], and outcome prediction [108][109][110][111][112] have also seen remarkable enhancements. These advancements have paved the way for highly personalized cancer treatments, offering a ray of hope within the continually evolving healthcare landscape [113][114][115]. Although ML has been applied in various treatment approaches in cancer medicine, its use in plasma medicine is relatively limited (Figure 1).…”