# Machine Failure Prediction Using Survival Analysis
## 6 Discussion  
The results show that the RSF model performs better than the other models selected for this study in predicting the main cause of machine failure, achieving a C-index of 0.9724. In general, the feature selection layer had a mostly positive impact on the C-index score in all the models, as depicted in Table 6. The table was calculated using the method described in Section 5.1.2. The most impressive increase was seen in the COX PH model, which more than doubled its C-index score. Furthermore, the model that seemed to be favored the most by the addition of the feature selection layer was RSF, which is the only one that either increased or kept the same C-index score. In contrast, the addition of the feature selection layer exhibited a negative impact on the prediction of the HDF failure type in three out of the four models that were tested. The most significant decrease of this failure type was noted in the ST model, whose C-index score decreased by approximately 31%. This shows that further research may be needed to address this issue in this specific type of failure, as it seems to be more susceptible to changes in the data.  
One notable finding from our study is that most models demonstrated improved accuracy in predicting failure types that were previously challenging to evaluate, suggesting a substantial enhancement in their predictive capabilities. This improvement is attributed to the incorporation of a feature analysis and selection layer, which effectively identified and leveraged the most relevant features for predicting these challenging failure types. This was mainly in the RNF and TWF failure types, where the models demonstrated lower C-index scores. For RNF, all models showed increases of over 14%--apart from the ST model, which had a slight decrease of 0.1%. Regarding TWF, the models that had the worst performance, namely the RSF and ST, had an increase of 25.65% and 8.9%, respectively. These increases in C-index score in the models show that the feature selection layer critically improves the overall prediction, helping it reach a more consistent prediction accuracy among all the failure types. It must be noted that the limited number of features provided in the dataset used may be a restricting element of the overall increase that the feature selection layer can provide to the model. Moreover, as emphasized in the Results section, the decrease in the C-index score of the HDF failure type is of major importance, and further research is needed to address this issue.  
One limitation of this study is the exclusive use of a single dataset. While the dataset was selected based on its suitability for addressing the research questions and objectives, the findings may not be generalizable to other populations or contexts. Additionally, the dataset contained a high number of right-censored data, with 96.61% (Table 1) of the instances being censored. This could impact the accuracy and reliability of the results, as well as limit the methods that were used. It is important for future research to consider using multiple datasets and exploring alternative methods for addressing censored data to improve the validity and generalizability of the findings.