's Data Mining: Foundations and Intelligent Paradigms, Volume PDF

There are lots of worthwhile books to be had on facts mining idea and functions. notwithstanding, in compiling a quantity titled “DATA MINING: Foundations and clever Paradigms: quantity three: scientific, healthiness, Social, organic and different Applications” we want to introduce many of the most modern advancements to a vast viewers of either experts and non-specialists during this field.

Data mining is among the so much swiftly growing to be examine parts in computing device technological know-how and information. In quantity three of this 3 quantity sequence, now we have introduced jointly contributions from essentially the most prestigious researchers in utilized information mining. components of software coated are varied and comprise healthcare and finance. all the chapters is self contained. Statisticians, utilized scientists/ engineers and researchers in bioinformatics will locate this quantity helpful. also, it presents a sourcebook for graduate scholars attracted to the present path of analysis in utilized facts mining.

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Read or Download Data Mining: Foundations and Intelligent Paradigms, Volume 3: Medical, Health, Social, Biological and other Applications (Intelligent Systems Reference Library, Volume 25) PDF

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Extra info for Data Mining: Foundations and Intelligent Paradigms, Volume 3: Medical, Health, Social, Biological and other Applications (Intelligent Systems Reference Library, Volume 25)

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1 The most obvious health risk confronted by a payer1 is pricing risk. A health plan accepts pricing risk by agreeing to reimburse health-related services (limited only by medical necessity and any other restrictions defined in the insurance contract) in exchange for a fixed monthly premium. This pricing risk can take one (or both) of two forms that coincide with the two risk concepts defined earlier – loss amount and probability. A health insurer’s actual experience can be equal to the expected number of claims, but at the same time include more large claims (severity).

The performance of KEA is comparable to that of Extractor. Song et al. proposed KPSpotter, which employs a new technique combining data mining techniques called Information Gain and several Natural Language Processing techniques such as stemming and case-folding (Song 2003). A disadvantage of supervised approaches is that they require a lot of training data and they are constrained by any specific training documents. Unsupervised approaches could be a possible alternative in this regard. The unsupervised approaches eliminate the need of training data.

First, we see clearly that the existence or absence of a health condition is correlated with deviation from the standardized (average) member cost in the last column. Members at any age that have no conditions cost less than the standardized cost for the age/sex group. Members with chronic or acute conditions cost considerably more. com 38 I. Duncan Table 4. Condition Based vs. Standardized Costs Member Age Sex Condition Actual Cost (annual) Standardized Condition Cost cost/standard (age/sex) ized cost 1 25 M None $1,247 $1,894 66% 2 55 F None $4,137 $6,994 59% 3 45 M Diabetes $7,257 $3,679 197% 4 55 F Diabetes $10,098 $6,994 144% 5 6 40 40 M M Diabetes and Heart conditions Heart condition $33,914 $26,267 $3,679 $3,679 922% 714% 7 40 F Breast Cancer and other conditions $38,605 $4,863 794% $23,016 $9,166 251% $60,243 $6,309 955% 8 60 F 9 50 M Breast Cancer and other conditions Lung Cancer and other conditions This example also illustrates the principle of additivity: • • • The 40 year old male whose only health risk is a heart condition experiences a cost of $26,267.

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