Abstract:

One of the main reasons for cancer-related fatalities in the world is lung cancer. Early identification the key to enhancing patient outcomes and survival rates. In this study, a deep learning-based early lung cancer prediction and heart disease method is introduced. The system uses a patient dataset of different disease of heart and lung to build a deep learning model that can precisely forecast a patient's risk of acquiring lung cancer within a specific timeframe. On a different test dataset, the model is tested, and it achieves great precision and accuracy. Clinical professionals may find the approach to a useful tool to identify patients who are at a high risk of getting lung cancer and to facilitate early identification and treatment which will eventually improve patient outcomes.

Keywords: dataset of lung and heart disease , cnn algorithm

Introduction:

This project brings in a feasible solution to the most commonly faced problem on lung cancer detection and heart disease across the world. An early lung cancer prediction system and heart disease prediction is a piece of software that estimates person's risk of acquiring lung cancer and heart disease using deep learning methods. One of the most common cancers to cause death worldwide is lung cancer and heart disease , and early detection can greatly enhance treatment outcomes and survival rates. In order to assess the risk of developing lung cancer, the early lung cancer prediction system considers a number of variables, including age, gender, smoking history, family history , and exposure to environmental toxins . The system analyses large datasets using data analytics and predictive modelling techniques to spot patterns and trends that can be used to estimate the likelihood of developing lung cancer and heart disease. The early lung cancer prediction system aims to assist healthcare providers in making informed decisions about lung cancer screening and prevention strategies. The system can also help individuals identify their risk factors for lung cancer and heart disease take necessary steps to reduce their risk of developing the disease. According to the information given by WHO 9.6 million individuals are assessed to have kicked the bucket overall because of disease in 2018. Also, 3 lakh new malignant growth cases analyzed every year are among youngsters matured 0-19 years. Malignant growth is among the deadliest infections that a human can get impacted. The positive side to it is that in the event that the malignant growth is identified at an early stage, then around half of the diseases can be forestalled and cured. Otherwise, it might prompt an extremely basic circumstance and may try and cause death . This makes it significantly more important to have a framework or innovation that can assist specialists with identifying malignant growth at the beginning phase where it tends to be dealt with really. To tackle this issue utilizing progressed innovative arrangements and man-made brainpower, a disease expectation framework utilizing. This procedure targets helping specialists and pathologists to identify disease at a beginning phase where it tends to be forestalled and restored, thereby saving many lives. The malignant growth illness expectation web application is an end client support online conference project. Based on the outcome calculation framework naturally shows results explicit specialists for additional treatment. This approach entails gathering and preparing text datasets pertaining to early lung cancer and heart disease prediction Medical professionals and researchers can learn more about the causes of early lung cancer and heart disease by employing this technique , which may also help them identify and treat patients earlier, improving their chances of a positive outcome for their health. Nonetheless, significant consideration must be given to the data chosen, feature extraction methods, and model selection when creating an accurate and trustworthy model.