Artificial Intelligence and Internet of Things Enabled Disease Diagnosis Model for Smart Healthcare Systems

Abstract

The recent advancements in Internet of Things (IoT), cloud computing and Artificial Intelligence (AI) transformed the conventional healthcare system into smart healthcare. By incorporating key technologies such as IoT and AI, medical services can be improved. The convergence of IoT and AI offers different opportunities in healthcare sector. The presented model encompasses different stages namely, data acquisition, pre-processing, classification, and parameter tuning. Heart disease is a major cause of morbidity and mortality globally and early detection is crucial for effective management. Machine learning models have been developed to aid in the prediction of heart disease with LightGBM being one such model. This study aims to analyse the performance of LightGBM in predicting heart disease. LightGBM was implemented using Python, and the model was trained using the training set. The performance of the model was evaluated using several metrics, including accuracy, precision, recall, F1 score, and area under the receiver operating characteristic (ROC) curve. Further studies could be conducted to evaluate the model’s performance on larger datasets and to compare its performance with other machine learning mode. Diseases may have an impact on people both physically and emotionally, since getting and living with an illness can change a person’s outlook on life. An illness that affects several areas of an organism yet is not caused by an instant exterior damage. Diseases are frequently defined as medical disorders characterised by distinct symptoms and indicators. The most lethal illnesses in humans are arteria coronary disease, cerebrovascular disease and lower respiratory infections. Heart disease is the most unexpected and unpredictability. With machine learning, we can anticipate cardiac disease. To get high efficiency output, we employ CNN approaches.