Predicting Diabetes Mellitus With Machine Learning Techniques Using Multi-Criteria Decision Making

Juneja, Abhinav; Juneja, Sapna; Kaur, Sehajpreet; Kumar, Vivek

doi:10.4018/ijirr.2021040103

Cited by 25 publications

(14 citation statements)

References 25 publications

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“…Stress in crops deals with external situations which adversely influence the development or productivity of plants [21]. A plant stress generally reflects a few sudden modifications in the climate situation.…”

Section: Stressesmentioning

confidence: 99%

Exploration of Crucial Factors Involved in Plants Development Using the Fuzzy AHP Method

Upadhyay¹,

Juneja²,

Turabieh

et al. 2022

Mathematical Problems in Engineering

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Remarkable changes in the environment have been impacting the production ability of plants. Few variation strategies are available to adapt for the outcomes of environmental changes such as maximum and minimum temperature in the development phase of the plants. A large portion of the plants expresses affectability to saltiness of the dirt. Considering the tediousness of various kinds, it is needful to perceive the possible obstructions in plants development. However, it has been observed that critical factors in plants development are required to be explored. The prime objective of this current work is for analysis of the chosen critical factors in plants development. Nine critical factors in plants development are shortlisted from the extensive literature reviewing in three categories, “climate factors”, “terrestrial factors”, and “human factors” with the help of experts’ inputs. Fuzzy analytical hierarchy process (FAHP) methodology has been selected for ranking of critical factors in plants development. Raw information is gathered from an online survey from twenty-five botanists, academicians, researchers, and agriculture scientists. The results may become very helpful in the strategy in developing efficient planning for dealing with critical factors in plants development.

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Section: Stressesmentioning

confidence: 99%

Exploration of Crucial Factors Involved in Plants Development Using the Fuzzy AHP Method

Upadhyay¹,

Juneja²,

Turabieh

et al. 2022

Mathematical Problems in Engineering

Self Cite

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“…In the survey for our current work, we observed that most of the sensors were generally employed for the prediagnosis and classification of the disease type. We were motivated to implement an integrated environment for dementia patients after the detection of disease to ease the daily activities [ 25 ].…”

Section: Related Workmentioning

confidence: 99%

A Perspective Roadmap for IoMT-Based Early Detection and Care of the Neural Disorder, Dementia

Juneja¹,

Dhiman²,

Kautish³

et al. 2021

Journal of Healthcare Engineering

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The Internet of Medical Things (IoMT) has emerged as one of the most important key applications of IoT. IoMT makes the diagnosis and care more convenient and reliable with proven results. The paper presents the technology, open issues, and challenges of IoMT-based systems. It explores the various types of sensors and smart equipment based on IoMT and used for diagnosis and patient care. A comprehensive survey of early detection and postdetection care of the neural disorder dementia is conducted. The paper also presents a postdiagnosis dementia care model named “Demencare.” This model incorporates eight sensors capable of tracking the daily routine of dementia patient. The patients can be monitored locally by an edge computing device kept at their premises. The medical experts may also monitor the patients’ status for any deviation from normal behavior. IoMT enables better postdiagnosis care for neural disorders, like dementia and Alzheimer’s. The patient’s behavior and vital parameters are always available despite the remote location of the patients. The data of the patients may be classified, and new insights may be obtained to tackle patients in a better manner.

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“…This dataset includes some features that need to be performed in lab like low-density lipoprotein (LDL), and highdensity lipoprotein (HDL). Juneja, A., et al in [12] used the PIMA Indian Diabetes dataset which as we mentioned before includes some features that require lab tests. Muhammad, L. J., et al in [13] used a dataset they collected from the Murtala Mohammed Specialist Hospital, Kano State, in Nigeria, and this dataset also included some features that are lab-related like HDL and triglyceride.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Model to Diagnose Diabetes Type 2 Based on Health Behavior

Alshari

Odabaş

2022

Gazi University Journal of Science

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Diabetes, in 2016, was the 7th death-causing disease in the world. It was the direct cause of 1.6 million deaths. In 2019, the number of adults (20-79 years) that were living with diabetes was approximately 463 million and is expected to rise to 700 million in 2045. The early diagnosis of diabetes will help treat it and prevent its complications. The need for an easy and fast way to diagnose diabetes is crucial. In this study, we are proposing a method to diagnose diabetes with the help of machine learning algorithms and tools. The proposed method utilizes the power of machine learning to create a model that can predict diabetes based on the health behavior of the patient. The model uses the relationship between a healthy lifestyle and diabetes. Our goal is to build a reliable machine learning model to predict diabetes, which will help significantly in easing and speeding up the diagnosing procedure of diabetes. We used modern machine learning algorithms like XGBoost, LightGBM, CatBoost, and artificial neural networks, and the dataset was obtained from the National Health and Nutrition Examination Survey (NHANES). In our study, the XGBoost algorithm performed the best with a Cross-Validation (10-fold) score of 0.864, and an overall accuracy of 87.7% for the validation dataset and 84.96% for the test dataset.

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Predicting Diabetes Mellitus With Machine Learning Techniques Using Multi-Criteria Decision Making

Cited by 25 publications

References 25 publications

Exploration of Crucial Factors Involved in Plants Development Using the Fuzzy AHP Method

Exploration of Crucial Factors Involved in Plants Development Using the Fuzzy AHP Method

A Perspective Roadmap for IoMT-Based Early Detection and Care of the Neural Disorder, Dementia

Machine Learning Model to Diagnose Diabetes Type 2 Based on Health Behavior

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