Medical disease prediction using Artificial Neural Networks

Mantzaris, Dimitrios; Anastassopoulos, George; Lymberopoulos, Dimitrios

doi:10.1109/bibe.2008.4696782

Cited by 44 publications

(36 citation statements)

References 18 publications

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“…Similar approaches exist within the medicine field today, where it is used for helping clinicians identify which people are at increased risk for osteoporosis and should therefore undergo further testing with bone densitometry (cf. Mantzaris et al 2008).…”

Section: Present Results To the Intelligence Analystmentioning

confidence: 99%

“…Research extends beyond the public safety and the police, also, including for medical data analysis and business forecasting (Liang & Austin 2005). An example of such use in the medicine field is presented by Mantzaris & Anastassopoulos (2008), where they look to help clinicians identify individuals with increased risk for osteoporosis that need to undergo further testing and treatment.…”

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confidence: 99%

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EXPLORING THE POTENTIAL FOR USING ARTIFICIAL INTELLIGENCE TECHNIQUES IN POLICE REPORT ANALYSIS - A Design Research Approach

2012

Proceedings of the 4th International Conference on Agents and Artificial Intelligence

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Section: Present Results To the Intelligence Analystmentioning

confidence: 99%

mentioning

confidence: 99%

EXPLORING THE POTENTIAL FOR USING ARTIFICIAL INTELLIGENCE TECHNIQUES IN POLICE REPORT ANALYSIS - A Design Research Approach

2012

Proceedings of the 4th International Conference on Agents and Artificial Intelligence

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“…Artificial neural network (ANN) has been implemented to identify brain diseases like Parkinson's, Schizophrenia, and Huntington's disease from the CNV response in electroencephalograph (Jervis et al, 1994). Multi layer perceptrons (MLPs) and probabilistic neural network (PNN) have been studied for prediction of osteoporosis with bone densitometry (Mantzaris et al, 2008). Similar analysis has been done for hepatitis (Uttreshwar and Ghatol, 2009), cancer (Lisboa et al, 2007), and other diseases.…”

Section: Background Workmentioning

confidence: 99%

Disease management: clustering-based disease prediction

Kaushik

Kapoor²,

Varadharajan

et al. 2014

IJCENT

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Abstract:The latest trends in healthcare industry have made enormous data available which is useful for disease prediction. This may be utilised by provider/payer for disease management study (DMS) for judging the present health condition and analysing disease trends. The present approach depends upon objective analysis which is based on binary value of symptoms and health indicators and direct relation between them. We propose a framework based on analytics to diagnose and predict diseases using enhanced genetic clustering using patient history, symptoms, and existing medical condition. New patient's profile is analysed against existing patterns and mapped to a particular cluster based on the patient's medical parameters. After cluster formation, cohesion and adhesion between the clusters is determined, which helps to predict present medical condition and probability for being prone to a disease in future. This framework provides an efficient disease management based on the preliminary diagnosis of the patient. 70 K. Kaushik et al.Keywords: disease management; disease prediction; disease analysis; clustering; healthcare; big data analytics.Reference to this paper should be made as follows: Kaushik, K., Kapoor, D., Varadharajan, V. and Nallusamy, R. (2014) Biographical notes: Kulvaibhav Kaushik working in research stream at Infosys Limited, has a BTech in Computer Science from IIT Roorkee. He has worked on mobile money, merchant transactions, healthcare, big data analytics, cloud computing, and gamification. Much of his research has been carried out over network security, elliptical curve cryptography, and searchable encryption. He has filed patents for methods of secure data storage at cloud and multiuser encryption methodology. He has a few publications in IEEE and others and has delivered keynote address at various national and international conferences. He is also an editor for a mobile payments magazine.Divya Kapoor is a confident healthcare Quality Analyst with more than five years of experience in insurance and provider sector. She is an American health insurance primer Part A and part B certified professional. She has worked on various information technology systems in claim adjudication, revenue cycle, management, and hospital ecosystem.Vijayaraghavan Varadharajan is Senior Research Scientist at Infosys Limited where he is doing research in the field of security assessment, watermarking techniques, network security, and security in converged networks. He has published many papers in international journals and conference proceedings and authored few patents in the area of network security. He has also refereed for many journals and served as a TPC Member of several IEEE and ACM international conferences. He holds a Bachelor of Engineering degree in EC Engineering, MTech in Advanced computing and PhD degree in Information and communication engineering.Rajarathnam Nallusamy is currently the Founder and Managing

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“…ANNs have been widely implemented over the last years in many scientific areas, like the industry [2], image processing [3], communications [4] and medicine [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

“…ANNs have been used in many medical areas successfully, such as cardiology [8], oncology [9], orthopaedics [5], urology [9], surgery [6] etc.…”

Section: Introductionmentioning

confidence: 99%

A Soft Computing Approach for Osteoporosis Risk Factor Estimation

Mantzaris

Anastassopoulos

Iliadis

et al. 2010

IFIP Advances in Information and Communication Technology

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Abstract. This research effort deals with the application of Artificial Neural Networks (ANNs) in order to help the diagnosis of cases with an orthopaedic disease, namely osteoporosis. Probabilistic Neural Networks (PNNs) and Learning Vector Quantization (LVQ) ANNs, were developed for the estimation of osteoporosis risk. PNNs and LVQ ANNs are both feed-forward networks; however they are diversified in terms of their architecture, structure and optimization approach. The obtained results of successful prognosis over pathological cases lead to the conclusion that in this case the PNNs (96.58%) outperform LVQ (96.03%) networks, thus they provide an effective potential soft computing technique for the evaluation of osteoporosis risk. The ANN with the best performance was used for the contribution assessment of each risk feature towards the prediction of this medical disease. Moreover, the available data underwent statistical processing using the Receiver Operating Characteristic (ROC) analysis in order to determine the most significant factors for the estimation of osteoporosis risk. The results of the PNN model are in accordance with the ROC analysis and identify age as the most significant factor.

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Medical disease prediction using Artificial Neural Networks

Cited by 44 publications

References 18 publications

EXPLORING THE POTENTIAL FOR USING ARTIFICIAL INTELLIGENCE TECHNIQUES IN POLICE REPORT ANALYSIS - A Design Research Approach

EXPLORING THE POTENTIAL FOR USING ARTIFICIAL INTELLIGENCE TECHNIQUES IN POLICE REPORT ANALYSIS - A Design Research Approach

Disease management: clustering-based disease prediction

A Soft Computing Approach for Osteoporosis Risk Factor Estimation

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