Analysis of Early Cancer Diagnosis Using Machine Learning

Abstract

Cancer is a group of diseases with similar symptoms, all involving uncontrolled growth and reproduction of cells. With around 8 million deaths each year, it is the second leading cause of death worldwide in developing countries and the first in the developed world. In contemporary medicine, early cancer diagnosis for every known type is essential. Machine learning has the potential to completely transform the process and increase the number of lives saved. In order to make predictions, computers develop complex data models and search for patterns. Early cancer diagnosis could undergo a revolution because of machine learning. This research’s goal is to outline the issue surrounding cancer diagnoses in patients and all the difficulties they experience. A suitable strategy will be to model the risk of cancer and patient outcomes given the growing trend of employing machine learning technics in cancer research. A specific model has been developed that, if applied appropriately, can reduce the number of lost lives and, at the same time, increase the number of individuals capable of coping with this disease. The results indicate that the created model can be used by professionals to identify lung cancer with efficiency. If the prediction is accurate, the doctor may be able to develop a better treatment plan and provide the patient with an early diagnosis. The study's findings show that the number of patients has been rising recently, yet early detection is crucial because it can help avert serious complications.