Cancer Prediction Using Machine Learning Techniques Based on Clinical & Non-Clinical Parameters
Abstract
Cancer is a serious illness that is characterized by the abnormal growth of cells in the body. Various parameters are considered for diagnosing any disease in a patient, which can be classified into clinical and non-clinical parameters. The development of machine learning technologies has led to an incredibly scientifically powerful process for solving various complexes, minimally complex biological problems. The intent of this work is early prediction of cancer based on various clinical and non-clinical parameters. It's also clear that the use of machine learning methods will boost the knowledge of cancer development, so our model will help in resolving this problem. Six classifiers have been applied on the primary data set taken from a private hospital in India and analysed for their performance based on F1-Score. The experiments are performed using clinical and non-clinical parameters separately as well as collectively. The results show a comparison of all the three models designed.