The ongoing threat that cancer poses to the
health and prosperity of the world highlights the critical
need for early identification and efficient treatment.
Machine learning and artificial intelligence have become
effective tools for the early detection of diseases like
cancer. The K-Nearest Neighbors (KNN) method stands
out among them for its efficiency and simplicity. The goal
of this study is to use the R implementation of the KNN
algorithm to advance the identification of prostate cancer.
A difficult diagnostic challenge is presented by the
complicated and multifaceted illness of prostate cancer.
This work seeks to develop precision medicine in oncology
by improving the accuracy and reliability of prostate
cancer detection using the capabilities of KNN. The study
examines the prostate cancer detection landscape, presents
the KNN algorithm's uses in medicine, and describes the
study's goals. The KNN model is trained and tested using a
dataset that has been pre-processed and used in this
methodology. The findings have the potential to
revolutionize the detection of prostate cancer by offering a
data-driven strategy to supplement healthcare
professionals' clinical judgement, thereby improving
patient outcomes, and even saving lives.
Keywords : Prostate Cancer, Early Detection, Machine Learning, K-Nearest Neighbors (KNN), Precision Medicine, Diagnosis, Artificial Intelligence.