Designing Disease Prediction Model Using Machine Learning Approach

Dahiwade, Dhiraj; Patle, Gajanan; Meshram, Ektaa

doi:10.1109/iccmc.2019.8819782

Cited by 176 publications

(62 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Following general disease prediction, this method is capable of presenting a risk associated with general disease that is lower or higher risk of general disease. [7].…”

Section: Related Workmentioning

confidence: 99%

Analysis of Alzheimer Disease Prediction Using Machine Learning Techniques

Hemalatha¹,

Renukadevi²

2021

ITII

View full text Add to dashboard Cite

Alzheimer's Disease (AD) is referred to as one of the highest non-unusual neurodegenerative disorders that inflict eternal harm to the memory-associated brain cells and wonder skills. There is a 99.6 percent failure rate in clinical trials of Alzheimer's disease pills, perhaps due to the fact that AD sufferers cannot be without early-stage complications. This observation analyzed machine learning knowledge of strategies to use empirical statistics to forecast the progression of AD in the years of fate. Diagnosis of AD is often difficult, particularly at an early stage in the disease system, due to the degree of mild cognitive impairment (MCI). However, it is at this point where treatment is much more likely to be successful, so there will be great benefits in enhancing the diagnosis process. Research in this area aims to identify the most complex mechanisms directly related to changes in AD. Various imaging methods are used to diagnose AD, and image modes play a key role in the diagnosis of AD. This paper uses a Positron Emission Tomography (PET) image to detect AD early. The PET image is often used to know how organs and tissues function in the human body. This research study analyses prediction approaches using various kinds of machine learning algorithms to solve AD diagnostic problems. Artificial Neural Networks are one of the many algorithms. Modern research has shown that deep learning is a proficient technique for solving numerous problems of image recognition, but most of these published approaches owe their performance to training on a very large number of data samples.

show abstract

“…Following general disease prediction, this method is capable of presenting a risk associated with general disease that is lower or higher risk of general disease. [7].…”

Section: Related Workmentioning

confidence: 99%

Analysis of Alzheimer Disease Prediction Using Machine Learning Techniques

Hemalatha¹,

Renukadevi²

2021

ITII

View full text Add to dashboard Cite

show abstract

“…For example, recent research show that the accuracy of predicting many diseases using CNN is greater than, for example, the KNN and NB algorithms. [4].…”

Section: Management Decision-making In Healthcarementioning

confidence: 99%

Methodology for Preprocessing Semi-Structured Data for Making Managerial Decisions in the Healthcare

Makarova

Lagerev

2020

Proceedings of the 30th International Conference on Computer Graphics and Machine Vision (GraphiCon 2020). Part 2

View full text Add to dashboard Cite

This paper describes the process of supporting management decisionmaking in healthcare based on data mining. The authors described various problems and specifics of data in medical information systems, leading to the complexity of their analysis and integration, such as: the presence of a large number of specific abbreviations, errors in the data and their poor structure. The paper demonstrates an approach to the search and further disclosure of abbreviations in texts, built on a combination of machine and human processing. A method for extracting features from semi-structured fields using an expert in the subject area and using various visualizations is proposed. The proposed abbreviation search and disclosure methods, based on a hybrid approach combining the strengths of processing with the help of a machine and an expert, can increase the number of abbreviations found automatically and significantly reduce the time spent by experts on processing the remaining reductions. In addition, the method for automated feature extraction during integration can significantly increase the amount of useful input data, while reducing the time of the expert.

show abstract

“…Several studies have demonstrated the usefulness of machine learning (ML) in predicting the onset of certain diseases. 14,15 In particular, ML-related methods are suitable for predictions based on existing data, 13 which suggests its potential applicability to the field of LTC. However, such tools have yet to be implemented.…”

Section: Introductionmentioning

confidence: 99%

Development and validation of gradient boosting decision tree models for predicting care needs using a long-term care database in Japan

Lin¹,

Fujiwara²,

Minoru³

et al. 2021

Preprint

View full text Add to dashboard Cite

ObjectiveThe purpose of the study was to develop machine learning models using data from long-term care (LTC) insurance claims and care needs certifications to predict the individualized future care needs of each older adult.MethodsWe collected LTC insurance-related data in the form of claims and care needs certification surveys from a municipality of Kanagawa Prefecture from 2009 to 2018. We used care needs certificate applications for model generation and the validation sample to build gradient boosting decision tree (GBDT) models to classify if 1) the insured’s care needs either remained stable or decreased or 2) the insured’s care needs increased after three years. The predictive model was trained and evaluated via k-fold cross-validation. The performance of the predictive model was observed in its accuracy, precision, recall, F1 score, area under the receiver-operator curve, and confusion matrix.ResultsLong-term care certificate applications and claim data from 2009–2018 were associated with 92,239 insureds with a mean age of 86.1 years old at the time of application, of whom 67% were female. The classifications of increase in care needs after three years were predicted with AUC of 0.80.ConclusionsMachine learning is a valuable tool for predicting care needs increases in Japan’s LTC insurance system, which can be used to develop more targeted and efficient interventions to proactively reduce or prevent further functional deterioration, thereby helping older adults maintain a better quality of life.

show abstract

Designing Disease Prediction Model Using Machine Learning Approach

Cited by 176 publications

References 9 publications

Analysis of Alzheimer Disease Prediction Using Machine Learning Techniques

Analysis of Alzheimer Disease Prediction Using Machine Learning Techniques

Methodology for Preprocessing Semi-Structured Data for Making Managerial Decisions in the Healthcare

Development and validation of gradient boosting decision tree models for predicting care needs using a long-term care database in Japan

Contact Info

Product

Resources

About