Hybrid Machine Learning Approach for Accurate Lung Cancer Prediction Using Structured Data and Medical Imaging - S. Ramanjaneyulu, S. Ramanjaneyulu; Neelima, Sadineni; Sushma, Sunkara; Babu ketha, Mahesh; Satya Prasad, Pennada Siva; Geddam, Gowripushpa (Journal of Neonatal Surgery, 2025)
A good prediction of lung cancer addresses predicting the right amount of cancer. Cancer is a primary reason of death compared to all diseases. Health information systems are central to universal healthcare globally. Accurate and required data is crucial in public healthcare decision-making, healthcare sector analysis, planning, resource allocation, and monitoring and program evaluation. Cancer is now the most dangerous kinds of disease among the living organisms in our world. In our world, humans are suffering from various cancers, in our world the worldwide burden of cancer has been estimated to have increased. The leading origin of similar cancer mortality is lung cancer. Only when lung-cancer has progressed does it show symptoms. Initial finding of lung cancer is probable with machine learning and deep learning. Our earlier system, one of the biggest issues with health care organizations are employing biological process to detect the cancer but the physicians were unable to find the right results which are shortening lives. In medical science the patient is undergoing various diagnostic tests, early diagnosis needs a precise and reliable diagnostic method that enables physicians to differentiate lung, breast and other cancer diseases from dangerous ones. Deep neural network is dominant tool for discovery such type of deceases. In the analysis deliberate the different techniques and it’s arrangement. Hybrid strategy is employed by combining structured data i.e. patient symptoms, medical history with medical images corresponding Computed Tomography scans and X-rays. Structured data is classified by Decision Tree, Random Forest and Extreme Gradient Boosting, whereas medical images are processed by Convolutional Neural Networks. A fusion strategy is utilized to fuse both modalities for an accurate classification model. Therefore, by the assistance of these models we are going to create a precise classification model of cancer prediction for the patient. Application of data mining classification procedures for successful forecast of future events with implementation of the problems encountered by existing system.