Intelligent Personalized Abnormality Detection for Remote Health Monitoring

Machine learning algorithms are extensively used in healthcare analytics to learn normal and abnormalpatternsautomatically.Thedetectionandpredictionaccuracyofanymachinelearning modeldependsonmanyfactorslikegroundtruthinstances,attributerelationships,modeldesign, thesizeofthedataset, thepercentageofuncertainty,thetrainingandtestingenvironment,etc. Predictionmodelsinhealthcareshouldgenerateaminimalfalsepositiveandfalsenegativerate. Toaccomplishhighclassificationorpredictionaccuracy,thescreeningofhealthstatusneedsto bepersonalized rather than followinggeneralclinicalpracticeguidelines (CPG)which fits for anaveragepopulation.Hence,apersonalizedscreeningmodel(IPAD–IntelligentPersonalized AbnormalityDetection)forremotehealthcareisproposedthattailoredtospecificindividual.The severityleveloftheabnormalstatushasbeenderivedusingpersonalizedhealthvaluesandthe IPADmodelobtainsanareaunderthecurve(AUC)of0.907. KeywoRDS Genetic Algorithm, Probabilistic Approach, Remote Health Monitoring, Severity Level Vital Health Parameter

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