Matches in SemOpenAlex for { <https://semopenalex.org/work/W4387253237> ?p ?o ?g. }
Showing items 1 to 74 of
74
with 100 items per page.
- W4387253237 endingPage "509" @default.
- W4387253237 startingPage "501" @default.
- W4387253237 abstract "In this paper, we will observe the usage of machine learning algorithms for the detection of lung cancer. In a country like India and especially in a city like Bilaspur or State like Chhattisgarh, the condition to take care of disease like lung cancer is very poor. The people who get this disease have to go to big cities for their treatment and in state like Chhattisgarh people are also not very rich. If the disease is detected at a concluding stage, the chance of saving the person is very less. We in the current work taking a sample experimental dataset from a standard repository the description of the fields and other factors of which are described in the paper have made an implementation in Python for detection of the disease. We, in the paper, made consideration of various algorithms on the dataset and got different values for the different assessment parameters. Based on the comparative analysis and looking to the utility that ML algorithm that turns out to be the best, we propose the usage of ML for detection of lung cancer. The literature survey part comprises of various work done in the field and data regarding the research in the field. In the paper, we are trying to propose ML approach and the current computing technology available to handle the problem of lung cancer. Generally, we are more concerned for the people of our state where medication facility is still not that good and early detection could help in saving life. In the current work, we made a utilization of Python which is a great data analysis language. Currently, people around the universe are working in this tool to have their work done in the desired way. In the current paper, we will look into the implementation of several machine learning algorithms. The algorithms that we observe in the implementation have proven their feats in the past and that’s why had been considered in the current paper. We will also have a look on the lung cancer paradigm which is the subject of implementation in the current paper. The paper is nicely drafted and is having introduction, problem statement, literature review, implementation, and result and conclusion as the main sections of consideration. The paper covers a wide spectrum of papers in which it made a good detailed survey of the various computer generated work for the solution of problems of various types. The paper is altogether a very nice work that could be handy for the research that is going to be put up in the time to come. We will observe a good detailed work on lung cancer and to continue in the journey will see a lot on Python and the various machine learning algorithms." @default.
- W4387253237 created "2023-10-03" @default.
- W4387253237 creator A5055943581 @default.
- W4387253237 creator A5058935905 @default.
- W4387253237 date "2023-10-03" @default.
- W4387253237 modified "2023-10-03" @default.
- W4387253237 title "Proposing ML Approach for Detection of Lung Cancer" @default.
- W4387253237 cites W1578275582 @default.
- W4387253237 cites W2474060356 @default.
- W4387253237 cites W2606462040 @default.
- W4387253237 cites W2786774834 @default.
- W4387253237 cites W2811466737 @default.
- W4387253237 cites W2915599927 @default.
- W4387253237 doi "https://doi.org/10.1007/978-981-99-4713-3_48" @default.
- W4387253237 hasPublicationYear "2023" @default.
- W4387253237 type Work @default.
- W4387253237 citedByCount "0" @default.
- W4387253237 crossrefType "book-chapter" @default.
- W4387253237 hasAuthorship W4387253237A5055943581 @default.
- W4387253237 hasAuthorship W4387253237A5058935905 @default.
- W4387253237 hasConcept C111919701 @default.
- W4387253237 hasConcept C119857082 @default.
- W4387253237 hasConcept C121608353 @default.
- W4387253237 hasConcept C126322002 @default.
- W4387253237 hasConcept C142724271 @default.
- W4387253237 hasConcept C154945302 @default.
- W4387253237 hasConcept C185592680 @default.
- W4387253237 hasConcept C198531522 @default.
- W4387253237 hasConcept C202444582 @default.
- W4387253237 hasConcept C2776256026 @default.
- W4387253237 hasConcept C2779134260 @default.
- W4387253237 hasConcept C2985322473 @default.
- W4387253237 hasConcept C33923547 @default.
- W4387253237 hasConcept C41008148 @default.
- W4387253237 hasConcept C43617362 @default.
- W4387253237 hasConcept C519991488 @default.
- W4387253237 hasConcept C71924100 @default.
- W4387253237 hasConcept C9652623 @default.
- W4387253237 hasConceptScore W4387253237C111919701 @default.
- W4387253237 hasConceptScore W4387253237C119857082 @default.
- W4387253237 hasConceptScore W4387253237C121608353 @default.
- W4387253237 hasConceptScore W4387253237C126322002 @default.
- W4387253237 hasConceptScore W4387253237C142724271 @default.
- W4387253237 hasConceptScore W4387253237C154945302 @default.
- W4387253237 hasConceptScore W4387253237C185592680 @default.
- W4387253237 hasConceptScore W4387253237C198531522 @default.
- W4387253237 hasConceptScore W4387253237C202444582 @default.
- W4387253237 hasConceptScore W4387253237C2776256026 @default.
- W4387253237 hasConceptScore W4387253237C2779134260 @default.
- W4387253237 hasConceptScore W4387253237C2985322473 @default.
- W4387253237 hasConceptScore W4387253237C33923547 @default.
- W4387253237 hasConceptScore W4387253237C41008148 @default.
- W4387253237 hasConceptScore W4387253237C43617362 @default.
- W4387253237 hasConceptScore W4387253237C519991488 @default.
- W4387253237 hasConceptScore W4387253237C71924100 @default.
- W4387253237 hasConceptScore W4387253237C9652623 @default.
- W4387253237 hasLocation W43872532371 @default.
- W4387253237 hasOpenAccess W4387253237 @default.
- W4387253237 hasPrimaryLocation W43872532371 @default.
- W4387253237 hasRelatedWork W2327204559 @default.
- W4387253237 hasRelatedWork W2587671147 @default.
- W4387253237 hasRelatedWork W2623240261 @default.
- W4387253237 hasRelatedWork W2961085424 @default.
- W4387253237 hasRelatedWork W3129254793 @default.
- W4387253237 hasRelatedWork W3157439253 @default.
- W4387253237 hasRelatedWork W4286629047 @default.
- W4387253237 hasRelatedWork W4306321456 @default.
- W4387253237 hasRelatedWork W4306674287 @default.
- W4387253237 hasRelatedWork W4224009465 @default.
- W4387253237 isParatext "false" @default.
- W4387253237 isRetracted "false" @default.
- W4387253237 workType "book-chapter" @default.