Medical Internet of things using machine learning algorithms for lung cancer detection

2020 ◽  
Vol 7 (4) ◽  
pp. 591-623 ◽  
Author(s):  
Kanchan Pradhan ◽  
Priyanka Chawla

Lung Cancer is the most general type of disease in theworld ofcancer. It affects the lungs of the human body. So, the prediction of lung cancer at its earlier stage is difficult. It is the deadliest cause of death in both men and women. Its symptoms are harder to recognize in the initial stages.Machine learning algorithms have made the prediction and detection of lung cancereasier. Chi-square is used for feature selection to select the relevant features in the lung cancer dataset. Different Machine Learning algorithms are used to predict Lung Cancer.The algorithmsutilized in the proposed work are SVM and Random Forest. We have compared these algorithms with and without feature selection (Chi-square). SVM is identified as the best algorithm in the proposed work due to its accuracy and less execution time for detecting the model. The key objective of this paper is to enhance the accuracy and reduce the execution time of the classifier.


The most lethal disease found in the medical field is lung cancer and early detection of this disease has become a challenge for many doctors and diagnostics. The lung cancer contributes over 15.3% of the total number of new cases diagnosed in the recent years. Smoking and pollution are considered as the major causes of lung cancer. At present, there are huge number of tests available to detect lung cancer such as PET Scan, Computerized Tomography (CT) Scan and X-ray etc. are used to diagnose the disease. By x-ray the picture of the lungs may uncover the unusual mass or nodule. A further developed adaption found in CT scan which can uncover the small lesions in the lung that probably won’t be distinguished with X-ray. Biopsy tests are done for detailed diagnosis of the disease. For accurate and better results, a data mining techniques, machine learning algorithms or deep learning algorithms could be used in the laboratories. In this survey, we have elaborated various existing techniques used so far.


Telecom IT ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 50-55
Author(s):  
D. Saharov ◽  
D. Kozlov

The article deals with the СoAP Protocol that regulates the transmission and reception of information traf-fic by terminal devices in IoT networks. The article describes a model for detecting abnormal traffic in 5G/IoT networks using machine learning algorithms, as well as the main methods for solving this prob-lem. The relevance of the article is due to the wide spread of the Internet of things and the upcoming update of mobile networks to the 5g generation.


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