MATERIALS AND METHODS: This was a retrospective study of testicular cancer patients treated between January 2001 and February 2011. Their epidemiological data, clinical presentation, pathologic diagnosis, stage of disease and treatment were gathered and the overall survival rate of this cohort was analyzed.
RESULTS: Thirty-one patients were included in this study. The majority of them were of Malay ethnicity. The average age at presentation was 33.7 years. The commonest testicular cancer was non-seminomatous germ cell tumour, followed by seminoma, lymphoma and rhabdomyosarcoma. More than half of all testicular germ cell tumour (GCT) patients had some form of metastasis at diagnosis. All the patients were treated with radical orchidectomy. Adjuvant chemotherapy was given to those with metastatic disease. Four seminoma patients received radiotherapy to the para-aortic lymph nodes. The 5-year survival rate for all testicular cancers in this cohort was 83.9%. The survival rate was 88.9% in 5 years when GCT were analyzed separately.
CONCLUSION: GCT affects patients in their third and fourth decades of life while lymphoma patients are generally older. Most of the patients treated for GCT are of Malay ethnicity. The majority have late presentation for treatment. The survival rate of GCT patients treated here is comparable to other published series in other parts of the world.
METHODS: This paper presents a new machine learning approach that uses MICE for mitigating missing data, the IQR for handling outliers and SMOTE to address first imbalance distance. Additionally, to select optimal features, we introduce the Hybrid 2-Tier Grasshopper Optimization with L2 regularization methodology which we call GOL2-2T. One of the promising methods to improve the predictive modelling is an Adaboost decision fusion (ABDF) ensemble learning algorithm with babysitting technique implemented for the hyperparameters tuning. The accuracy, recall, and AUC score will be considered as the measures for assessing the model.
RESULTS: On the results, our heart disease prediction model yielded an accuracy of 83.0%, and a balanced F1 score of 84.0%. The integration of SMOTE, IQR outlier detection, MICE, and GOL2-2T feature selection enhances robustness while improving the predictive performance. ABDF removed the impurities in the model and elaborated its effectiveness, which proved to be high on predicting the heart disease.
DISCUSSION: These findings demonstrate the effectiveness of additional machine learning methodologies in medical diagnostics, including early recognition improvements and trustworthy tools for clinicians. But yes, the model's use and extent of work depends on the dataset used for it really. Further work is needed to replicate the model across different datasets and samples: as for most models, it will be important to see if the results are generalizable to populations that are not representative of the patient population that was used for the current study.