A Review of Machine Learning’s Role in Cardiovascular Disease Prediction: Recent Advances and Future Challenges

Naser, Marwah Abdulrazzaq; Majeed, Aso Ahmed; Alsabah, Muntadher; Al-Shaikhli, Taha Raad; Kaky, Kawa M.

doi:10.3390/a17020078

Cited by 4 publications

References 222 publications

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A systematic review of factors, data sources, and prediction techniques for earlier prediction of traffic collision using AI and machine Learning

Niture,

Abdellatif

2024

Multimed Tools Appl

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The prevalence of road traffic collisions is a pressing issue both worldwide and within the United States. The consequences of these incidents are severe, resulting in loss of life, reduced productivity, and other socio-economic implications that demand immediate attention. To effectively address this problem, conducting an extensive literature review is crucial to identify the various causes of traffic collisions and the complex interdependencies between them. Addressing this challenge necessitates a targeted exploration of its multifaceted causes and their interrelations through an extensive literature review, incorporating the latest advancements in machine learning and deep learning techniques. However, the lack of a consensus on datasets and prediction techniques hinders the development of accurate, location-specific traffic collision predictions. By meticulously analyzing traffic collision factors and data sources and leveraging state-of-the-art ML and DL approaches, this paper endeavors to forge a pathway toward developing precise, location-adapted predictions for traffic collisions, thereby contributing significantly to the discourse on long-term preventative strategies.

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A systematic review of factors, data sources, and prediction techniques for earlier prediction of traffic collision using AI and machine Learning

Niture,

Abdellatif

2024

Multimed Tools Appl

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Heart Disease Detection Using Feature Extraction and Artificial Neural Networks: A Sensor-Based Approach

Naeem,

Senapati,

Bhuva

et al. 2024

IEEE Access

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This research proposes a successful technique for identifying individuals using feature extraction methods and signal processing approaches. The novelty of this study is in the use of feature extraction methods in conjunction with other signal-processing approaches. While working with the sensors, an artificial neural network (ANN) technique is employed to identify the scent patterns that are present in persons. The numerous gases released by the human body are measured using ten different kinds of sensors, all of which are metal oxide semiconductors. Before using ANN patterns to generate patterns from sensor data, it is important to scan and extract sensory information from that data. Each participant is recognized and scanned for a totally of 1000 different characteristics during the course of the multiple investigations, which are conducted across a variety of time periods that include 5, 10, 15, and 20 people. Because of the varying time periods, signals from sensors are received in analog form, which is then transformed by Arduino into digital form. It is necessary to train an architecture on the data set that has been created. The benchmarks that are employed for the assessment of the model that is presented for the identification of human odor include sensitivity, f-measures, accuracy, and specificity, among other things. Experiments are carried out using the assessment measures, and the findings demonstrate that this model has an accuracy of greater than 85 % in most cases.

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Cardiovascular Risk Assessment With Current Machine Learning Methods and Future Integration of Quantum Networks

Ramyasri,

Yoga,

Tamilarasu

et al. 2024

Advances in Computational Intelligence and Robotics

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Heart strokes represent a significant global health concern, affecting individuals across diverse age groups, including children and teenagers. By applying the power of machine learning and analyzing crucial indicators such as age, gender, body mass index, average glucose level, smoking habits, employment status, and living conditions, the authors aim to predict the occurrence of heart strokes even before they happen. Various machine learning techniques, including support vector machines (SVM), logistic regression, gaussian naive bayes, k-nearest neighbor's (KNN), decision trees, random forest, and XGBoost, are employed to classify an individual's stroke risk level. The assessment offers a comprehensive comparison of these algorithms, ultimately identifying the most effective approach. Additionally, the authors explore the integration of quantum networks to enhance the predictive capabilities of these machine learning models, potentially revolutionizing the accuracy and efficiency of heart stroke prediction.

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A Review of Machine Learning’s Role in Cardiovascular Disease Prediction: Recent Advances and Future Challenges

Cited by 4 publications

References 222 publications

A systematic review of factors, data sources, and prediction techniques for earlier prediction of traffic collision using AI and machine Learning

A systematic review of factors, data sources, and prediction techniques for earlier prediction of traffic collision using AI and machine Learning

Heart Disease Detection Using Feature Extraction and Artificial Neural Networks: A Sensor-Based Approach

Cardiovascular Risk Assessment With Current Machine Learning Methods and Future Integration of Quantum Networks

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