Using Machine Learning to Predict the Future Development of Disease

Miao, Lei; Guo, Xuezhou; Abbas, Hasan; Qaraqe, Khalid; Abbasi, Qammer H.

doi:10.1109/ucet51115.2020.9205373

Cited by 18 publications

(6 citation statements)

References 16 publications

(17 reference statements)

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“…While the ensemble algorithms used in this study (XGBoost and AdaBoost) outperformed the Naive Bayes algorithm, it's important to note that their performance differences were not significant. Furthermore, the best-performing model in this study fell significantly short in terms of accuracy when compared to the study conducted by Miao et al [7]. Specifically, our study achieved an accuracy rate of 71%, whereas Miao et al reported a much higher accuracy of 96.9%.…”

Section: B Models Developmentcontrasting

confidence: 86%

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Supervised Machine Learning Approach for Predicting Cardiovascular Complications Risk in Patients with Diabetes Mellitus

Muharram,

Sajid

2024

Preprint

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Diabetes mellitus, particularly type-2 diabetes, remains a prevalent health issue, raising concerns due to its associated risk of complications. Among these, cardiovascular complications pose a significant threat, exhibiting high morbidity and mortality rates. Health screening plays a pivotal role in stratifying the risk levels of diabetes patients, facilitating proactive measures to prevent the progression of complications. As such, the primary objective of this study is to develop a predictive model system for assessing cardiovascular risk in diabetes patients. Our study used the Cardiovascular Disease dataset and conducts experiments with various supervised machine learning algorithms, such as Naive Bayes, decision tree, random forest, AdaBoost, and XGBoost. The results reveal that ensemble learning algorithms based on boosting, particularly AdaBoost and XGBoost, outperform other supervised machine learning methods. However, even with the best performance achieved using the dataset, the accuracy stands at 0.71, and the F-1 score is 0.69, which is still acceptable for screening purposes. Although these results provide valuable insights, indicating individuals at higher risk for cardiovascular complications in diabetes, further improvements are needed to enhance early prevention strategies.

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Section: B Models Developmentcontrasting

confidence: 86%

“…Another study was conducted by Miao et al [7]. In their research, they utilized the Framingham Heart Study dataset along with Support Vector Machine (SVM) and k-Nearest Neighbors (KNN) algorithms to predict cardiovascular disease in T2DM patients.…”

Section: Related Workmentioning

confidence: 99%

Supervised Machine Learning Approach for Predicting Cardiovascular Complications Risk in Patients with Diabetes Mellitus

Muharram,

Sajid

2024

Preprint

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“…The concept of prediction using longitudinal data is of great importance in the medical field. There are works in the literature that exploit ML and DL for prediction tasks even outside the field of HF [27,28].…”

Section: Related Workmentioning

confidence: 99%

Deep Learning for Predicting Congestive Heart Failure

et al. 2022

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Congestive heart failure (CHF) is one of the most debilitating cardiac disorders. It is a costly disease in terms of both lives and financial outlays, given the high rate of hospital re-admissions and mortality. Heart failure (HF) is notoriously difficult to identify on time, and is frequently accompanied by additional comorbidities that further complicate diagnosis. Many decision support systems (DSS) have been developed to facilitate diagnosis and to raise the standard of screening and monitoring operations, even for non-expert staff. This is confirmed in the literature by records of highly performing diagnosis-aid systems, which are unfortunately not very relevant to expert cardiologists. In order to assist cardiologists in predicting the trajectory of HF, we propose a deep learning-based system which predicts severity of disease progression by employing medical patient history. We tested the accuracy of four models on a labeled dataset, composed of 1037 records, to predict CHF severity and progression, achieving results comparable to studies based on much larger datasets, none of which used longitudinal multi-class prediction. The main contribution of this work is that it demonstrates that a fairly complicated approach can achieve good results on a medium size dataset, providing a reasonably accurate means of determining the evolution of CHF well in advance. This potentially constitutes a significant aid for healthcare managers and expert cardiologists in designing different therapies for medication, healthy lifestyle changes and quality of life (QoL) management, while also promoting allocation of resources with an evidence-based approach.

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“…Model forecasts without the costly and cumbersome examination of oral glucose tolerance. Following testing on the Framingham Heart Study dataset, the model yielded high-performance outcomes [5].…”

Section: Related Workmentioning

confidence: 99%

Diagnosis and Classification of Diabetes Mellitus Type 1 and Type 2 Through Super Learner

Nisha

Kavitha

2021

Preprint

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Diabetes Mellitus (DM) plays a significant role in increasing the associated health problems worldwide by acting as a Comorbid condition. Moreover, it is a progressive illness without severe external symptoms leading to a fatal impact on the human body if left unnoticed or untreated. This research work aims to associate an individual’s lifestyle and ethnic background in assessing the risk of Diabetes acting as a comorbid condition. A detailed assessment of lockdown impact with rapid modification in individual’s lifestyle due to the pandemic gives specific insight into individuals becoming susceptible to Diabetes Mellitus. An ensemble of ML algorithms is utilized in predicting the risk of individuals turning Diabetic. The ensemble of the ML model is trained on the Pima Indian dataset and Vanderbilt biostatistics diabetes dataset providing the impact of Type 1 diabetes mellitus. The proposed super learner model provides the highest classification accuracy of T1DM & T2DM with 97% compared to an ensemble of algorithms in identifying and classifying the individuals as being susceptible to DM due to the lifestyle and ethnic background.

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Using Machine Learning to Predict the Future Development of Disease

Cited by 18 publications

References 16 publications

Supervised Machine Learning Approach for Predicting Cardiovascular Complications Risk in Patients with Diabetes Mellitus

Supervised Machine Learning Approach for Predicting Cardiovascular Complications Risk in Patients with Diabetes Mellitus

Deep Learning for Predicting Congestive Heart Failure

Diagnosis and Classification of Diabetes Mellitus Type 1 and Type 2 Through Super Learner

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