Machine Learning in Medical Diagnosis of Cancer
Rishi Raj, Jyoti Jyoti, Antara Singh, Karan Kumar · 2024
This study compares machine learning (ML)-based medical diagnosis techniques with conventional medical diagnosis techniques. The first section of the study gives background information on conventional medical diagnosis techniques and the difficulties—such as poor accuracy, inefficiency, and high cost—that they encounter. The use of ML technology for medical diagnostics is then explained, along with some of its possible advantages, including improved accessibility, efficiency, and accuracy. The process for creating and implementing an ML-based medical diagnosis system is then described in the study, along with data collection and analysis methods. The analysis's findings demonstrate that ML-based medical diagnostic systems are superior to conventional techniques in a number of ways, including increased accuracy, quicker diagnosis times, and reduced costs.The study also compares the system's performance with conventional medical diagnosis techniques and assesses its effectiveness based on a number of criteria. According to the study, ML-based medical diagnosis systems provide a number of important benefits over conventional techniques, including improved early-stage cancer detection and more precise prognosis prediction. In conclusion, the research findings and their consequences are discussed, future directions for research are suggested, and the study's limitations are emphasized. Overall, this work offers insightful information on the possibilities of machine learning for medical diagnostics and establishes a foundation for future investigations.