Matches in SemOpenAlex for { <https://semopenalex.org/work/W4386427146> ?p ?o ?g. }
Showing items 1 to 80 of
80
with 100 items per page.
- W4386427146 abstract "Tobacco consumption is a major public health concern because it is associated with a range of serious health problems, including lung cancer, heart disease, stroke, respiratory disease, and many others. Tobacco consumption is a leading cause of preventable death and disease worldwide, and it is estimated to be responsible for over 7 million deaths each year. The primary active ingredient in tobacco products is nicotine, which is highly addictive. People who use tobacco products can become physically dependent on nicotine, which can make it very difficult to quit using tobacco. Too much of nicotine consumption can cause the staining of the dental enamel. The aim of this study is to develop machine learning (ML) model to classify chronic diseases in person based on their dental staining. The study is divided into two phases. In the first phase involves the development of a smart system to detect and categorize the dental stain as high, average and low. This phase also involves the collection of demographic data and its analysis to derive valuable insights from it. The second phase involves training of the different ML models to recognize the hidden pattern in them. The parameters of the ML models are optimized by tuning and then the tuned ML models are employed for classifying the chronic illness. The Grid Searched Logistic Regression (GSLR) showed the best prediction out of all the ML models. The GSLR model showed training and testing accuracy of 64% and 60% respectively. The applicability of the model is ascertained by applying the model to the test data. The GSLR model is able to correctly classify 7 out of the 31 cases of no chronic diseases and 38 out of 44 cases of chronic diseases. Based on the applicability of the model, it can be concluded that the model is capable of detecting chronic diseases with dental enamel staining as an indicator." @default.
- W4386427146 created "2023-09-05" @default.
- W4386427146 creator A5042112008 @default.
- W4386427146 creator A5080751844 @default.
- W4386427146 date "2023-07-14" @default.
- W4386427146 modified "2023-09-30" @default.
- W4386427146 title "Teeth Staining from Tobacco Consumption as an Indicator to Chronic Illness: A Data Analytics and Machine Learning Application" @default.
- W4386427146 cites W1982549375 @default.
- W4386427146 cites W1994526481 @default.
- W4386427146 cites W2034588875 @default.
- W4386427146 cites W2042595123 @default.
- W4386427146 cites W2066977846 @default.
- W4386427146 cites W2117127611 @default.
- W4386427146 cites W2611722398 @default.
- W4386427146 cites W2903727731 @default.
- W4386427146 cites W3003935185 @default.
- W4386427146 cites W3015117186 @default.
- W4386427146 cites W3022967271 @default.
- W4386427146 cites W3040455124 @default.
- W4386427146 cites W3047698802 @default.
- W4386427146 cites W3119469335 @default.
- W4386427146 cites W3151286594 @default.
- W4386427146 cites W3212700429 @default.
- W4386427146 cites W4220868306 @default.
- W4386427146 cites W4297546313 @default.
- W4386427146 cites W4318041816 @default.
- W4386427146 cites W6157354 @default.
- W4386427146 doi "https://doi.org/10.1109/wconf58270.2023.10234993" @default.
- W4386427146 hasPublicationYear "2023" @default.
- W4386427146 type Work @default.
- W4386427146 citedByCount "0" @default.
- W4386427146 crossrefType "proceedings-article" @default.
- W4386427146 hasAuthorship W4386427146A5042112008 @default.
- W4386427146 hasAuthorship W4386427146A5080751844 @default.
- W4386427146 hasConcept C118552586 @default.
- W4386427146 hasConcept C119857082 @default.
- W4386427146 hasConcept C142724271 @default.
- W4386427146 hasConcept C144024400 @default.
- W4386427146 hasConcept C151956035 @default.
- W4386427146 hasConcept C154945302 @default.
- W4386427146 hasConcept C2779134260 @default.
- W4386427146 hasConcept C2779547902 @default.
- W4386427146 hasConcept C30772137 @default.
- W4386427146 hasConcept C36289849 @default.
- W4386427146 hasConcept C41008148 @default.
- W4386427146 hasConcept C48856860 @default.
- W4386427146 hasConcept C71924100 @default.
- W4386427146 hasConcept C94124525 @default.
- W4386427146 hasConcept C99454951 @default.
- W4386427146 hasConceptScore W4386427146C118552586 @default.
- W4386427146 hasConceptScore W4386427146C119857082 @default.
- W4386427146 hasConceptScore W4386427146C142724271 @default.
- W4386427146 hasConceptScore W4386427146C144024400 @default.
- W4386427146 hasConceptScore W4386427146C151956035 @default.
- W4386427146 hasConceptScore W4386427146C154945302 @default.
- W4386427146 hasConceptScore W4386427146C2779134260 @default.
- W4386427146 hasConceptScore W4386427146C2779547902 @default.
- W4386427146 hasConceptScore W4386427146C30772137 @default.
- W4386427146 hasConceptScore W4386427146C36289849 @default.
- W4386427146 hasConceptScore W4386427146C41008148 @default.
- W4386427146 hasConceptScore W4386427146C48856860 @default.
- W4386427146 hasConceptScore W4386427146C71924100 @default.
- W4386427146 hasConceptScore W4386427146C94124525 @default.
- W4386427146 hasConceptScore W4386427146C99454951 @default.
- W4386427146 hasLocation W43864271461 @default.
- W4386427146 hasOpenAccess W4386427146 @default.
- W4386427146 hasPrimaryLocation W43864271461 @default.
- W4386427146 hasRelatedWork W194496750 @default.
- W4386427146 hasRelatedWork W2014016697 @default.
- W4386427146 hasRelatedWork W2015940952 @default.
- W4386427146 hasRelatedWork W2365213443 @default.
- W4386427146 hasRelatedWork W2389328397 @default.
- W4386427146 hasRelatedWork W2748952813 @default.
- W4386427146 hasRelatedWork W2899084033 @default.
- W4386427146 hasRelatedWork W2961085424 @default.
- W4386427146 hasRelatedWork W4231752535 @default.
- W4386427146 hasRelatedWork W4319601155 @default.
- W4386427146 isParatext "false" @default.
- W4386427146 isRetracted "false" @default.
- W4386427146 workType "article" @default.