As it can been seen in the Tables 3.1 to 3.5, we have tried 3 popular machine learning algorithms to build joint and stage-specific survivability models. As we discussed earlier, these algorithms are the most popular techniques to build survivability models. We compared the performance of stage-specific models, created with these three algorithms to figure out which algorithm gives a model with better performance.
Table 3.6: Algorithms comparison result forbreast cancersurvivability predictive mod- els.
Comparison result
Localized AD3 >Logistic regression >Na¨ıve Bayes Regional Logistic regression>Na¨ıve Bayes,
AD3 >Na¨ıve Bayese
Distant No statistically significant differences found between used algorithms
Table 3.7: Algorithms comparison result for colon cancersurvivability predictive mod- els.
Comparison result
Localized Logistic regression >AD3 >Na¨ıve Bayes Regional Logistic regression >Na¨ıve Bayes,
Logistic regression >AD3
Distant Logistic regression >Na¨ıve Bayes
Table 3.8: Algorithms comparison result for lung & bronchus cancer survivability predictive models.
Comparison result
Localized Logistic regression >AD3 >Na¨ıve Bayes Regional Logistic regression >AD3 >Na¨ıve Bayes Distant Logistic regression >AD3
In the Tables 3.6 to 3.10, one can see the results of these comparisons. For some models, we did not find any statistically significant difference (p value less than 0.05). We considered two-tailed paired t-test to compare the corresponding values (values at the same row but obtained from different machine learning techniques). The results are denoted by using the “greater than” symbol (>). Therefore, where the algorithm is statistically significantly better than the other algorithm, we expressed it greater than the other.
From the Tables 3.6 to 3.10, it can be seen that logistic regression and AD3 generally do better performance on localized and regional stages in building survivability prediction models than na¨ıve Bayes for cancers in the study (except for corpus uteri cancer). In general, there is not much difference between them on distant stage instances.
Table 3.9: Algorithms comparison result forcorpus uteri cancersurvivability predictive models.
Comparison result Localized Na¨ıve Bayes >AD3,
Na¨ıve Bayes >Logistic regression Regional Na¨ıve Bayes >AD3,
Logistic regression >AD3
Distant Na¨ıve Bayes >Logistic regression
Table 3.10: Algorithms comparison result for stomach cancer survivability predictive models.
Comparison result
Localized AD3 >Logistic regression >Na¨ıve Bayes Regional AD3 >Logistic regression >Na¨ıve Bayes Distant No statistically significant differences found
Chapter 4
Conclusion and Outlook
In past, only joint survival prediction models were built by training machine learning methods on all the stages together. They were also evaluated together on all the stages. In [33] stage-specific models were built for breast cancer survivability and it was reported that joint models offer no advantage over stage-specific models for predicting breast cancer survivability and are often worse. It was also reported that evaluating on all stages together, as was always done in past, leads to an overestimation of performance.
In this study, we investigated whether the above observations made about breast can- cer in [33] also generalizes to other cancers. To this end, we built joint and stage-specific survivability predictive models for five cancers - breast, colon, lung & bronchus, corpus uteri, and stomach. We used SEER dataset along with three machine learning algorithms - na¨ıve Bayes, logistic regression, and decision tree to make the survivability predictive models. According to the obtained results, joint models do not provide better perfor- mance than stage-specific models, and in most cases, their performance are statistically significantly worse than the stage-specific models. We also saw that the order of impor- tance of features, as determined using information gain statistic, is different for each stage which further supports building separate models for every stage instead of building joint models. Based on these results, we recommend building separate survivability predictive models for separate stages. We also found that the evaluation of models on all stages together is misleading, since it tends to overestimate the performance. Therefore, to see the real performance of a model, we recommend evaluating its performance for each stage separately. Hence we found that the observations reported in [33] for breast cancer also generalize to other cancers.
We also saw that there is no specific pattern to suggest using a specific algorithm to make accurate predictive models for cancer survivability, but all the algorithms used here
can give a reasonable performance. However we note that na¨ıve Bayes requires much less computational time and the models generated with logistic regression and AD3 were better for most cancers. We also observed that the performance of most models was generally worse on the distant stage. In future, more training data of that stage may help improve the performance.
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