Paper Title
Machine Learning Approach to Predict Lung Cancer using CT Scan Images

Abstract
Machine learning methods have become a popular tool for medical researchers to find the accurate prediction. Machine learning techniques can identify the patterns in complex data set and it can be effectively predict the cancer. In the recent medical era, medical imaging plays an essential role to perform effective diagnosis and analysis of the complete human body. Medical experts completely depends the medical imaging results which are obtained from the image sensors. Cancer is the most dangerous life taking disease in the world. As World Health Organization (WHO) reported, 9 million people died the cancer in all around the world. Today medical diagnosis process needs computational methods to find the results with improved accuracy. Machine learning techniques are widely used to support medical practitioners to find the diseases in earlier stage. The CT scan images are used along with computational methods gives better results than normal tests. Early cancer detection plays a vital role for human health in the healthcare industry. In the human body respiration system, lungs are the fundamental element. Lung cancer seems to be a very common cause of death among people all over the world. The accurate prediction of the lung cancer is the very challenging task for medical practitioners. In this research work, real patient CT scan images are collected from Lung Image Database Consortium (LIDC) archive. The main objective of this research work is to develop a Machine learning based Computer Aided Diagnosis (CAD) system to ensure early diagnosis of lung cancer. The proposed machine learning based classification system is also differentiating the tumors between benign and malignant. Keywords - Machine Learning, Cancer Prediction, Classification, Cancer Susceptibility.