The development and use of prediction models is increasing in many domains, including health care. These models can be used to guide clinical decision-making as they can potentially provide more accurate predictions of outcomes by adjusting for multiple patient and clinical features, compared to clinician assessment alone [1,2]. Machine learning (ML) methods are increasingly being used to develop prediction models as their use often yields models with superior predictive performance compared to models developed using traditional statistical methods [3]. However, there have been growing concerns about the potential for bias in ML models, as studies have demonstrated discrepancies in model performance among different population subgroups, which can lead to disparities in health care [4-6]. This concern for model bias, or an unfair model, has brought attention to the concept of algorithm fairness, where a fair model is described as one that works well for all populations, regardless of personal characteristics that are considered protected or sensitive, such as sex, race, age, sexual orientation, among others [7,8].

An unfair model is of particular concern in bladder cancer, where disparities in the likelihood of receiving treatment and timing of treatment have been identified in sex and racial subgroups [9]. While several predictive models have been developed to predict survival in patients with bladder cancer [10-12], the performance of these models among different population subgroups remains understudied. Predicting survival is an important component in counseling patients with bladder cancer given the relatively high mortality rates in this disease [13,14]. The use of a potentially unfair model may perpetuate and even exacerbate existing disparities for vulnerable patient populations.

In this study, we used the National Cancer Database (NCDB) to develop a ML model that predicts survival after radical cystectomy, the current standard treatment for muscle-invasive bladder cancer [15], and evaluated for potential model unfairness in sex and racial subgroups. We then applied and compared model unfairness mitigation techniques to improve algorithm fairness [16] and deployed the first web-based fair ML model predicting survival after radical cystectomy.

In this study, we used the NCDB to develop a ML model predicting survival after radical cystectomy for bladder cancer and found that the naïve model would be considered unfair due to inferior performance in certain sex and racial subgroups. We then compared and used algorithm unfairness mitigation techniques in the 3 different phases of model development and found that all techniques improved model fairness, with negligible impact on overall classifier accuracy. Our study highlights the importance of evaluating prediction models in health care for potential unfairness and demonstrates different techniques that can be applied to mitigate model unfairness. We also deploy the first web-based “fair” model predicting survival in patients undergoing radical cystectomy for muscle-invasive bladder cancer.

The potential consequences of algorithm unfairness have been highlighted in previous studies. In a study that evaluated software used by courts in the United States to decide whether to release an offender or to keep them in prison, they found higher FPRs for Black offenders, compared to White offenders [6]. The bias in this model is partly due to the skewed training data in which the 2 populations had unequal base rates of being charged with new crimes [32]. These discrepant base rates may be partially attributable to heavier policing in predominantly Black neighborhoods or bias in the decision to make an arrest [33]. Another study evaluated a model used by health insurers to select patients for complex care management programs and found that for the same level of model-predicted risk, Black patients have significantly higher illness burdens, compared to White patients [5]. This bias was attributed to the algorithm’s objective of accurately predicting costs, which biases against Black patients who have been shown to generate lower medical expenses, conditional on health. A review of clinical ML algorithms found that the majority of models exhibited some form of racial bias [28]. The application of these unfair models could amplify and perpetuate disparities for vulnerable populations. Fairness methods can be used to mitigate known biases; in the context of the prison software, for example, historical biases in the data could be addressed by developing a model to optimize demographic parity [8]. In the context of the biased complex care algorithm, fairness would be improved by having the model’s objective changed to accurately predict active chronic conditions, rather than costs [5].

In bladder cancer, several prediction models have been developed to predict survival [10-12], although none have been evaluated for potential unfairness in sex and racial subgroups. In this study, we found that a model predicting survival after radical cystectomy demonstrated unfairness whereby specific subgroups, such as Black male patients, had superior model performance compared to others, such as Asian female patients. If such an algorithm was used to support decision-making, quality-of-service harm may result in subgroups with less accurate predictions [31].

Identifying the source of model unfairness can be challenging, and several potential causes have been described [7,8,28,31]. Sampling bias occurs when the model performs worse in minority populations due to insufficient sampling during training [7]. This source of bias is of potential concern when developing prediction models in diseases such as bladder cancer, where the epidemiology is significantly skewed by sex and race; worldwide, bladder cancer is approximately 4 times more common in male patients than female patients [34] and in the United States, bladder cancer is approximately 2 times more common in White populations compared to Black, Hispanic, and Asian populations [35]. Given this skewed demographic, which was reflected in our study, a naïve ML model may not perform equally well in all subgroups of patients. However, despite Black male patients representing only 3.1% of our sample, we found that the naïve model performed best in this subgroup. Notably, Black male patients had the highest mortality rate at 5 years, and this imbalanced data may partly explain the superior model performance in this subgroup. This aligns with prior research where a ML model predicting survival in patients with prostate cancer undergoing radical prostatectomy showed superior performance in Black patients, which was also the minority group with the highest mortality rate in that study [4].

While the potential for model bias has been described in previous studies, research on applying techniques to mitigate algorithmic unfairness remains limited [28]. However, a recent scoping review found that all studies implementing methods to address racial bias successfully improved fairness metrics [28]. This underscores the value of using such techniques before model deployment to optimize fairness. Beyond metrics, ensuring that a model is perceived as fair is particularly important in health care as it can enhance trust among both patients and clinicians, potentially leading to greater acceptance and integration of the model’s predictions into clinical decision-making. Our study also highlights the importance of evaluating and comparing multiple techniques to achieve optimal fairness, as there was variability in the degree of improvement observed between each approach.

Our study is not without limitations. Despite the use of model unfairness techniques which improved the eORs of the naïve model, the final “fair” model did not achieve an eOR of 1, indicating the presence of residual unfairness; however, some might argue that an eOR of 1 is theoretical and could not be achieved in practice. This concept was illustrated in a study that demonstrated the restrictive conditions required for a deterministic classifier to achieve perfect equalized odds [36]. Therefore, while approaches can be used to reduce algorithmic unfairness, clinicians and patients should still be aware of residual potential discrepancies in model performance. Another limitation of optimizing eOR is that it violates other definitions of fairness and may compromise performance in certain subgroups [26]. In our study, we found that while the F₁-score of the mitigated model improved or was unchanged in some subgroups, performance decreased in certain subgroups, highlighting the potential trade-off between fairness and performance. This raises an ethically complex question regarding what is considered “fair,” and the answer varies depending on what conceptual framework of fairness is used. We specifically focused on the pillar of inclusion which seeks to ensure that the benefits and harms from prediction models are distributed in an equitable manner [37]. The eOR is also challenged as a metric in the context of imbalanced data, as is common in health care, as baseline differences can contribute to disparities in performance. However, despite the expected differences across sex and racial subgroups in our study, we prioritized the eOR to develop a model that satisfied equality of error rates across subgroups [8,27] and where true and false positive predictions of 5-year overall survival were of equal importance. Finally, our study uses NIH racial groups, which are composed of heterogeneous subgroups; we did not evaluate model performance in these subgroups given their expected limited sample size. Additionally, we chose to focus on sex and race given the existing disparities in bladder cancer among these subgroups [9], but other sensitive subgroups, such as those based on socioeconomic status or rurality, could be studied for potential bias in model performance. Despite these limitations, our study demonstrates the importance of evaluating model unfairness and, to the best of our knowledge, is the first to apply unfairness mitigation techniques to reduce disparities from potential algorithmic unfairness in bladder cancer.

In conclusion, we developed a ML model to predict survival after radical cystectomy for bladder cancer and found that a naïve model exhibited bias, as certain subgroups of patients, based on sex and race, had inferior performance compared to others. Using algorithm unfairness mitigation techniques to minimize potential disparities arising from biased models, we improved model fairness and deployed the first web-based fair ML model for predicting survival after radical cystectomy.