Matches in SemOpenAlex for { <https://semopenalex.org/work/W4298042915> ?p ?o ?g. }
- W4298042915 endingPage "304" @default.
- W4298042915 startingPage "289" @default.
- W4298042915 abstract "Studies have revealed that neurodegenerative diseases known to cause cognitive impairment will increase significantly worldwide by 2050. Subsequently, there is a critical need to utilize machine learning (ML) to analyze medical and health data to improve diagnosis, classification, and treatment of neurodegenerative diseases. With the rapid development of computational approaches, different knowledge-driven and data-driven models in artificial intelligence (AI) and clinical practice can be utilized to overcome challenges in understanding, diagnosing, and preventing neurodegenerative disease. The development of biosensor technologies that use preprocessing of data, data collection, ML classifiers, and feature extraction has allowed timely detection of many neurodegenerative diseases. Thus, this chapter elaborates on the importance of ML techniques for diagnosing and preventing neurodegenerative diseases." @default.
- W4298042915 created "2022-10-01" @default.
- W4298042915 creator A5002564924 @default.
- W4298042915 creator A5011259051 @default.
- W4298042915 creator A5015589994 @default.
- W4298042915 creator A5076481056 @default.
- W4298042915 creator A5091430372 @default.
- W4298042915 date "2023-01-01" @default.
- W4298042915 modified "2023-10-16" @default.
- W4298042915 title "Prevention and diagnosis of neurodegenerative diseases using machine learning models" @default.
- W4298042915 cites W1605886981 @default.
- W4298042915 cites W1761228750 @default.
- W4298042915 cites W1968698114 @default.
- W4298042915 cites W1971116939 @default.
- W4298042915 cites W1978763244 @default.
- W4298042915 cites W1980314710 @default.
- W4298042915 cites W1984625499 @default.
- W4298042915 cites W1987448986 @default.
- W4298042915 cites W2009082022 @default.
- W4298042915 cites W2012862746 @default.
- W4298042915 cites W2014418634 @default.
- W4298042915 cites W2025621238 @default.
- W4298042915 cites W2031358440 @default.
- W4298042915 cites W2037954483 @default.
- W4298042915 cites W2045732268 @default.
- W4298042915 cites W2054130199 @default.
- W4298042915 cites W2066116456 @default.
- W4298042915 cites W2076063813 @default.
- W4298042915 cites W2079672765 @default.
- W4298042915 cites W2080423736 @default.
- W4298042915 cites W2081126811 @default.
- W4298042915 cites W2108475007 @default.
- W4298042915 cites W2110759468 @default.
- W4298042915 cites W2126118258 @default.
- W4298042915 cites W2171831801 @default.
- W4298042915 cites W2307954101 @default.
- W4298042915 cites W2341719493 @default.
- W4298042915 cites W2403220016 @default.
- W4298042915 cites W2495874476 @default.
- W4298042915 cites W2525984666 @default.
- W4298042915 cites W2539246095 @default.
- W4298042915 cites W2549552700 @default.
- W4298042915 cites W2570634615 @default.
- W4298042915 cites W2588978745 @default.
- W4298042915 cites W2611631440 @default.
- W4298042915 cites W2726164105 @default.
- W4298042915 cites W2732823864 @default.
- W4298042915 cites W2734661081 @default.
- W4298042915 cites W2760972917 @default.
- W4298042915 cites W2776737272 @default.
- W4298042915 cites W2777453889 @default.
- W4298042915 cites W2790031975 @default.
- W4298042915 cites W2795761008 @default.
- W4298042915 cites W2797029508 @default.
- W4298042915 cites W2799837895 @default.
- W4298042915 cites W2811392751 @default.
- W4298042915 cites W2889197740 @default.
- W4298042915 cites W2890106926 @default.
- W4298042915 cites W2899314245 @default.
- W4298042915 cites W2904384642 @default.
- W4298042915 cites W2906155095 @default.
- W4298042915 cites W2914745067 @default.
- W4298042915 cites W2919115771 @default.
- W4298042915 cites W2937781457 @default.
- W4298042915 cites W2940616544 @default.
- W4298042915 cites W2955176339 @default.
- W4298042915 cites W2955791435 @default.
- W4298042915 cites W2956228567 @default.
- W4298042915 cites W2964657045 @default.
- W4298042915 cites W2976398475 @default.
- W4298042915 cites W2978944956 @default.
- W4298042915 cites W2980265635 @default.
- W4298042915 cites W2994875504 @default.
- W4298042915 cites W3006225682 @default.
- W4298042915 cites W3008414870 @default.
- W4298042915 cites W3022113644 @default.
- W4298042915 cites W3043037931 @default.
- W4298042915 cites W3043374725 @default.
- W4298042915 cites W3133618741 @default.
- W4298042915 cites W4235875658 @default.
- W4298042915 cites W4238388420 @default.
- W4298042915 doi "https://doi.org/10.1016/b978-0-323-90277-9.00009-2" @default.
- W4298042915 hasPublicationYear "2023" @default.
- W4298042915 type Work @default.
- W4298042915 citedByCount "0" @default.
- W4298042915 crossrefType "book-chapter" @default.
- W4298042915 hasAuthorship W4298042915A5002564924 @default.
- W4298042915 hasAuthorship W4298042915A5011259051 @default.
- W4298042915 hasAuthorship W4298042915A5015589994 @default.
- W4298042915 hasAuthorship W4298042915A5076481056 @default.
- W4298042915 hasAuthorship W4298042915A5091430372 @default.
- W4298042915 hasConcept C10551718 @default.
- W4298042915 hasConcept C119857082 @default.
- W4298042915 hasConcept C142724271 @default.
- W4298042915 hasConcept C154945302 @default.
- W4298042915 hasConcept C2522767166 @default.
- W4298042915 hasConcept C2779134260 @default.
- W4298042915 hasConcept C34736171 @default.