A Decision System for Predicting Diabetes using Neural Networks

Rani, K. Chandana; Yalla, Prasanth

doi:10.11591/ijai.v6.i2.pp56-65

Cited by 5 publications

(6 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…All these language models are based on N-gram approximation. Bayesian classifiers have been [19][20][21]. As opposed to Bayesian, classifier assumes no correlation between words in the same text, where N-gram language model assume relationships between the words, and evaluate the probability of a word being before or after another word.…”

Section: Proposed Methodsmentioning

confidence: 99%

An investigative design of optimum stochastic language model for bangla autocomplete

Eyamin

Habib

Islam

et al. 2019

IJEECS

View full text Add to dashboard Cite

<p class="Abstract">Word completion and word prediction are two important phenomena in typing that have extreme effect on aiding disable people and students while using keyboard or other similar devices. Such autocomplete technique also helps students significantly during learning process through constructing proper keywords during web searching. A lot of works are conducted for English language, but for Bangla, it is still very inadequate as well as the metrics used for performance computation is not rigorous yet. Bangla is one of the mostly spoken languages (3.05% of world population) and ranked as seventh among all the languages in the world. In this paper, word prediction on Bangla sentence by using stochastic, i.e. <em>N</em>-gram based language models are proposed for autocomplete a sentence by predicting a set of words rather than a single word, which was done in previous work. A novel approach is proposed in order to find the optimum language model based on performance metric. In addition, for finding out better performance, a large Bangla corpus of different word types is used.</p>

show abstract

Section: Proposed Methodsmentioning

confidence: 99%

An investigative design of optimum stochastic language model for bangla autocomplete

Eyamin

Habib

Islam

et al. 2019

IJEECS

View full text Add to dashboard Cite

show abstract

“…Enterprises use the trends of data analytics and AI embedded in enterprise advanced application typically used in large organization to manage resources and customer information. There are five common prediction techniques that mostly used to build predictive model namely neural network, decision tree, linear regression, association rule mining and support vector machine (SVM) [8][9][10][11][12][13]. The description below explains briefly of each technique with the references paper that used the techniques.…”

Section: Predictive Analytics and Techniquesmentioning

confidence: 99%

“…In [13] used images as the data to predict diabetes. The researcher used PDR images and test the model using probabilistic neural network (PNN), Bayesian classifier and SVM techniques.…”

Section: Decision Tree Classifiermentioning

confidence: 99%

Prevalence of hypertension: predictive analytics review

Nor

Mohamed

Mutalib

2020

IJ-AI

View full text Add to dashboard Cite

Hypertension is one of the non-communicable disease (NCD) that is classify as a global health risk with many critical health cases. Malaysia raise the same concern of the increasing NCD health problem. This paper aims to study the techniques used in predictive analytics namely healthcare and identify the factors of prevalence on hypertension. This review would give a better understanding of proper techniques and suggest the technique commonly used in predictive analytics especially for medical data and at the same time provide significant factors of prevalence hypertension. A total of 27 papers reviewed, several techniques on predictive analytics in healthcare are neural network, decision tree, naïve bayes, regression and support vector machine. The rise of economic growth and correlated socio-demographic have cause rise in hypertension problem over past years. The factors of hypertension depicted in this review namely gender, age, locality, family history, physically inactive and unhealthy life style not conform to any boundaries thus far. Thus, the choice on the technique and hypertension factors for predictive analytics is significant to come out with the significant predictive model. The predictive model on prevalence of hypertension may predict the severity of adult having hypertension in future work.

show abstract

“…Multi-layer perceptron (MLP) is often referred to the networks composing of several layers of the perceptron, it is feedforward ANN that comprises of three main layers, namely: the input layer, the output layer and the hidden layer [7]. On the reception of a certain weight, every input nodule in the input layer will be moved to the neurons [8]. Therefore, the capability of neural networks to learn patterns and form generalizations about virtually any kind of data make them appropriate for this task.…”

Section: Introductionmentioning

confidence: 99%

Off-line Handwritten Signature Verification System: Artificial Neural Network Approach

Tahir¹,

Ausat²,

Bature³

et al. 2021

IJISA

View full text Add to dashboard Cite

Nowadays, it is evident that signature is commonly used for personal verification, this justifies the necessity for an Automatic Verification System (AVS). Based on the application, verification could either be achieved Offline or Online. An online system uses the signature’s dynamic information; such information is captured at the instant the signature is generated. An offline system, on the other hand, uses an image (the signature is scanned). In this paper, some set of simple shaped geometric features are used in achieving offline Verification of signatures. These features include Baseline Slant Angle (BSA), Aspect Ratio (AR), and Normalized Area (NA), Center of Gravity as well as the line’s Slope that joins the Center of Gravities of the signature’s image two splits. Before the features extraction, a signature preprocessing is necessary to segregate its parts as well as to eliminate any available spurious noise. Primarily, System training is achieved via a signature record which was acquired from personalities whose signatures had to be validated through the system. An average signature is acquired for each subject as a result of incorporating the aforementioned features which were derived from a sample set of the subject’s true signatures. Therefore, a signature functions as the prototype for authentication against a requested test signature. The similarity measure within the feature space between the two signatures is determined by Euclidian distance. If the Euclidian distance is lower than a set threshold (i.e. analogous to the minimum acceptable degree of similarity), the test signature is certified as that of the claiming subject otherwise detected as a forgery. Details on the stated features, pre-processing, implementation, and the results are presented in this work.

show abstract

A Decision System for Predicting Diabetes using Neural Networks

Cited by 5 publications

References 7 publications

An investigative design of optimum stochastic language model for bangla autocomplete

An investigative design of optimum stochastic language model for bangla autocomplete

Prevalence of hypertension: predictive analytics review

Off-line Handwritten Signature Verification System: Artificial Neural Network Approach

Contact Info

Product

Resources

About