PREDICTION OF HEART DISEASE USING K-MEANS and ARTIFICIAL NEURAL NETWORK as HYBRID APPROACH to IMPROVE ACCURACY

  • Malav A
  • Kadam K
  • Kamat P
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Abstract

Employing the pioneering work of Charles Parrish as a basis of comparison, this study serves as a follow-up to " Color Names and Color Notions " by deconstructing the contemporary language and attitudes surrounding skin color. Nine focus groups with 58 black women between the ages of 18 and 25 reveal that the color names and color notions offered were consistent with many of the terms and stereotypes that Parrish found, thereby indicating that there has been no change in colorist ideology among African Americans. Participants discussed 40 color names regularly employed to describe light, medium, and dark skin tones. The terms and attitudes associated with light skin tones were generally negative; conversely, the terms and attitudes asso-ciated with dark skin tones were derogatory. The language and beliefs con-nected to medium skin tones indicate that colorism operates as a three-tiered structure rather than the traditionally situated binary paradigm.

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Malav, A., Kadam, K., & Kamat, P. (2017). PREDICTION OF HEART DISEASE USING K-MEANS and ARTIFICIAL NEURAL NETWORK as HYBRID APPROACH to IMPROVE ACCURACY. International Journal of Engineering and Technology, 9(4), 3081–3085. https://doi.org/10.21817/ijet/2017/v9i4/170904101

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