Quality of Life Predictors in Patients With Melanoma: A Machine Learning Approach

Abstract

Health related quality of life (HRQoL) is an important recognized health outcome for cancer treatments, but also disease course with slower recovery and increased morbidity. These issues are of implication in melanoma, which maintains a risk of disease progression for many years after diagnosis. This study aimed to explore and weigh factors in the perception of the quality of life and possible relationships with demographic–clinical characteristics in people with melanoma via a machine learning approach. In this observational study, patients with melanoma, without metastatic disease, were recruited from January 2020 to December 2021 with a follow-up of at least one year. Demographic variables and clinics were collected, and the 12-Item Short-Form Health Survey (SF-12) was adopted as the physical and mental aspects of the Health-Related Quality of Life (HRQoL) measure. All the variables were processed in a random forest model to weigh at each node of each tree of this machine learning regression model, their actual weight in SF-12 score. We included 203 melanoma patients, mean aged 59.25 ± 15.1 years: 56 (27%) affecting the upper limbs and 147 (73%) affecting the trunk. The model of 142 patients with no missing value, generating 92 trees (MSE = 0.45, R2 of 0.78), reported that the lesion site was the most influencing variable on HRQoL based on the decrease in Gini impurity in variable weighing at each node intersection in forest generation. In this scenario, we built two distinct models for lesion sites and demonstrated that the variable that most influenced the quality of life in upper limb melanoma was lymphedema, while BMI was in the trunk. Given these results, random forest regressions could play a crucial role in the clinical and rehabilitation approach. The machine-learning model for detecting the HRQoL predictor in melanoma patients indicates that the experienced lymphedema and BMI may influence the HRQoL perception. This study suggests that the prevention and treatment of lymphedema and bodyweight reduction might improve the quality of life in melanoma.

Development and Approval of a Multidimensional Model of the Clinical Effectiveness of Treatment Technologies for Patients With a Mild COVID-19 Associated With Comorbidities

“…Байєсівські методи також можуть використовуватися для моделювання невизначеності та включення попередньої інформації в математичне моделювання HRQoL, особливо в тих випадках, коли дані є обмеженими [50]. Підходи машинного навчання, такі як випадкові ліси, методи опорних векторів і глибоке навчання, можуть також бути використані для обробки результатів дослідження HRQoL для завдань передба-чення та класифікації [51]. Треба зазначити, що представлені підходи визначення якості життя потребують ґрунтовної підготовки в галузі прикладної математики та інформаційних технологій і часто є недосяжними для клінічного лікаря.…”

Immunotherapy in melanoma: advances, pitfalls, and future perspectives

“…In recent years, there has been an increasing focus on not only improvement of survival outcomes but also health-related quality of life (HRQoL), which can be seen as a set of long-term aspects concerning the social, psychological, emotional, and cognitive statuses of the patient that are impacted by the treatments ( Mamoor et al, 2020 ; Pinto et al, 2022 ). Indeed, despite the obvious advantages, treatment with ICIs is related to several toxic effects called immune-related adverse effects (irAEs) owing to immune activation and inflammatory responses against the healthy tissue of the host.…”

The Use of Artificial Intelligence in Orthopedics: Applications and Limitations of Machine Learning in Diagnosis and Prediction

“…The output is therefore a discrete one, and can assume only one of a limited amount of predefined values. The opposite is instead true for regression: this type of task indeed has the goal of predicting a continuous number [5,7]; an example of such Appl. Sci.…”