Precision Medicine in Oncology - Leveraging Machine Learning for Accurate Neoplasm Diagnosis

The precision medicine in oncology provided a novel era where the patients have better chances of being cured and improved outcomes if they adopted the personalized treatment strategies. The core part of this change is the utilization of the ML (machine learning) algorithms which showcases good specificity as compared to humans in making neoplasm diagnoses. This research paper will be exploring an application of ML approaches within precision medicine area for oncology patients and will be trying to fill-up the gap between big genome data and decision making processes in the clinical area. This work proposes a complete study which investigates the usefulness of several strategies of ML in diagnosis under cancer with the help of the detailed dataset that consists of genomic and clinicological profile of patients. In our study we investigated several types of ML algorithms covering decision trees, neural networks as well as support vector machines. Every algorithm underwent strict test and validation for its diagnostic power to be identified. Our approach inaugurated the use of ROC curves, precision-recall curves, as well as survival analysis to objectively evaluate model performance, along with feature importance rankings which allowed for identification of biomarkers as well as genetic signatures known to be predictive of specific cancer subtypes and treatment responses. ML work is seen to show a substantially higher sensitivity, specificity and accuracy compared to conventional diagnostic methods. Having ML integrated with precision medicine, makes the screening of cancer in early stages and the accurate diagnosis possible. Additionally, it provides the opportunity to devise personal treatment plans depending on the various genetic tumor types. Besides that, the results from survival analysis illustrated that these ML-based prognostic models can be used for accurately forecast patient outcomes. This has an implication of not only better management of existing treatment plan but also to making survival rates more promising.