Virtual Human for Assisted Healthcare: Application and Technology

Ali, Itimad Raheem; Ahmed, Ahmed Sabah; Tayyeh, Huda Kadhim; Kolivand, Hoshang; Alkawaz, Mohammed Hazim

doi:10.1007/978-3-319-08234-9_363-1

Cited by 2 publications

(1 citation statement)

References 32 publications

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“…The main tasks performed in the data preprocessing stage include: Removal of Redundancy, Noisy Data. Also, the preprocessing stage involved Feature Selection whereby only eight of the attributes collected (Age, Gender, HBA1C, TG, Urea, Chol, HDL and BMI) were considered, whereas, the less important features were ignored as their information gain is of no significance except for the Blood Sugar Level which was ruled out because it is the decisive factor in diabetes diagnosis [18], [19]. The data were applied using two different algorithms, and it was found that the level of accuracy was low.…”

Section: Construct Diabetes Datasetmentioning

confidence: 99%

Diabetes diagnosis system using modified Naive Bayes classifier

Alwan

Jaafar²,

Ali

2022

IJEECS

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<span>In today’s world, Diabetes is one of these diseases and is now a big growing health problem. The techniques of data mining have been widely applied to extract knowledge from medical databases. In this work, a Medical Diagnosis system of Diabetes is proposed for the ‎diagnosis of diabetes in a manner ‎that is rapid and cost-effective. three stages are ‎involved in the proposed diabetes diagnosis system (DDS) including: dataset constructing, preprocessing and classification algorithm using traditional Naïve Bayesian ‎‎(TNB) and modified Naïve Bayesian (MNB)). MNB Classifier is a modified NB that is used to ‎enhance the accuracy of ‎diagnosis, by adding a proposed modest model to help separate ‎the overlapping diagnosis classes. The outcome‎ ‎showed that the accuracy of MNB classifier is generally higher than that of ‎TNB ‎classifier for all sets of features. An accuracy of about (63%) was achieved for the TNB ‎model, whereas ‎that of the MNB model is (100%). The experimental results showed that ‎the MNB is better than the traditional ‎NB in both two cases of constructed medical ‎datasets; the first case of filling the missing values by experiences and ‎the second case of filling ‎missing values by K-nearest neighbor (KNN) algorithm.</span>

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Section: Construct Diabetes Datasetmentioning

confidence: 99%