Automated Health Alert System using machine learning
Praveena Nuthakki1, Madhavilatha Pandala2, Madhavi Katamaneni3
1Praveena Nuthakki Department of IT, VRSEC, Vijayawada, India.
2P.Madhavilatha , Department of IT, VRSEC, Vijayawada, India.
3K.Madhavi, Department of IT, VRSEC, Vijayawada, India.
Manuscript received on April 30, 2020. | Revised Manuscript received on May 06, 2020. | Manuscript published on May 30, 2020. | PP: 2445-2448 | Volume-9 Issue-1, May 2020. | Retrieval Number: A2945059120/2020©BEIESP | DOI: 10.35940/ijrte.A2945.059120
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The social insurance condition is commonly seen as being ‘data rich’ yet ‘information poor’. There is an abundance of information accessible inside the social insurance frameworks. Notwithstanding, there is an absence of powerful investigation apparatuses to find shrouded connections and patterns in information. Information revelation and information mining have discovered various applications in business and logical area. Important information can be found structure use of information mining strategies in medicinal services framework. The human services industry gathers enormous measures of medicinal services information which, lamentably, are not “mined” to find shrouded data. For information preprocessing and viable dynamic Naïve Bayes classifier is utilized. It is an augmentation of Naïve Bayes to uncertain probabilities that targets conveying strong characterizations additionally when managing little or deficient informational indexes. The HUI digger is utilized to locate the high utility thing sets from a database. Disclosure of shrouded examples and connections frequently gets unexploited. Utilizing clinical profiles, for example, age, sex, circulatory strain and glucose it can anticipate the probability of patients getting a coronary illness. It empowers huge information, for example designs, connections between clinical elements identified with coronary illness, to be set up
Keywords: Numerous, unexploited, imprecise
Scope of the Article: Machine Learning