Improving Multi-Organ Cancer Diagnosis through a Machine Learning Ensemble Approach

Numerous methods for feature selection and classification have been developed, and the process of identifying cancer becomes challenging when the quantity of the information is enormous. The machine learning-based ensemble technique was created to address the weaknesses of the previous methodology.With poor survival rates after diagnosis, cancer is a common cause of death that is reported in many industrialized and developing nations. The probability that cancer will be successfully treated rather than allowed to get worse increases with early identification and therapy. Identification of the afflicted regions and significant factors that influence cancer growth is the foundation for the majority of cancer diagnoses. There are several techniques for feature selection and classification, and when there is a large amount of data, finding cancer becomes difficult. The machine learning-based ensemble methodology was developed to solve the shortcomings of the earlier approach. This research has the potential to bring about significant advancements in healthcare by enhancing diagnostic accuracy, personalizing treatment, and improving patient outcomes while addressing ethical and fairness considerations. Its impact extends beyond the research domain, benefiting patients, healthcare systems, and the broader field of artificial intelligence in healthcare.