You raise an excellent point. An accuracy of 98% might seem impressive at first glance, but it's crucial to consider the context and the nature of the dataset when evaluating a model's performance, especially in critical domains like cancer detection.
In the given scenario, the dataset is likely imbalanced, meaning that the number of instances in each class (cancer vs. non-cancer) is significantly different. In such cases, relying solely on accuracy can be misleading, as you mentioned.
Here's why accuracy alone is not sufficient:
Accuracy can be heavily influenced by the majority class. If the dataset contains a large proportion of non-cancer instances compared to cancer instances, a model that simply predicts every instance as non-cancer can achieve high accuracy without actually being useful for detecting cancer cases.
In cancer detection, the cost of misclassifying a cancer case as non-cancer (false negative) is much higher than misclassifying a non-cancer case as cancer (false positive). A false negative can lead to delayed diagnosis and treatment, potentially putting the patient's life at risk.
To properly evaluate the model's performance, we should consider additional metrics that provide a more comprehensive view:
Sensitivity (True Positive Rate): It measures the proportion of actual cancer cases that are correctly identified by the model. A high sensitivity indicates that the model is effective in detecting cancer cases.
Specificity (True Negative Rate): It measures the proportion of actual non-cancer cases that are correctly identified by the model. A high specificity indicates that the model is effective in identifying non-cancer cases.
F1 Score: It is the harmonic mean of precision and recall. Precision measures the proportion of true positive predictions among all positive predictions, while recall is the same as sensitivity. The F1 score provides a balanced measure of the model's performance, considering both precision and recall.
Additionally, it's important to validate the model's performance using techniques like cross-validation and testing on an independent dataset to ensure its generalization ability.
In summary, while a 98% accuracy might seem promising, it's crucial to consider the imbalanced nature of the dataset and evaluate the model using metrics like sensitivity, specificity, and F1 score. These metrics provide a more comprehensive assessment of the model's performance in detecting cancer cases accurately. Before deploying the model in production, it should be thoroughly validated and tested to ensure its reliability and effectiveness in real-world scenarios.