An Efficient Heart Disease Prediction System Using Machine Learning

Swain, Debabrata; Ballal, Preeti; Dolase, Vishal; Dash, Banchhanidhi; Santhappan, Jayasri

doi:10.1007/978-981-15-1884-3_4

Cited by 19 publications

(10 citation statements)

References 3 publications

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“…It is also used for classi cation problems to summarize prediction results [38]. It consists of four main values: true positive (TP), true negative (TN), false positive (FP) and false negative (FN) [39]. The test accuracy obtained is about 99.53%.…”

Section: Confusion Matrixmentioning

confidence: 99%

Yoga Pose Monitoring System using Deep Learning

Swain

Satapathy

Patro

et al. 2022

Preprint

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Activity recognition is the process of continuously monitoring a person’s activity and movement. Human posture recognition can be utilized to assemble a self-guidance practice framework that permits individuals to learn and rehearse yoga postures accurately without help from anyone else. Using Deep learning algorithms, an approach has been taken to accurately detect and recognize various yoga poses. The chosen dataset consists of a total of 85 videos with 6 yoga postures performed by 15 participants. Initially, keypoints of the user are extracted using the Mediapipe library. A combination of convolutional neural network (CNN) and long short-term memory (LSTM) has been used for Yoga pose recognition through real-time monitored videos as a deep learning model. CNN layer is used for extraction of features from the keypoints and it is followed by LSTM that understands the occurrence of sequence of frames for predictions to be made. Then, the poses are classified as correct or incorrect. If a correct pose is identified, the system will give the user feedback through text/speech.

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Section: Confusion Matrixmentioning

confidence: 99%

Yoga Pose Monitoring System using Deep Learning

Swain

Satapathy

Patro

et al. 2022

Preprint

Self Cite

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“…Secondly, updating the biased first moment estimate is computed according to the below Eqs. (8) and (9).…”

Section: ̂= ( ( +1mentioning

confidence: 99%

“…The latest advancements in machine learning techniques improve the study in improving the system to diagnose heart disease. The existing models used CNN networks for the prediction of heart disease considered data from the large datasets [9]. The models having traffic data contained noise and uncertainty was unpredictable as the data ambiguity emerged.…”

Section: Introductionmentioning

confidence: 99%

A Big Data Analysis Using Fuzzy Deep Convolution Network Based Model for Heart Disease Classification

Manur¹,

Pani²,

Kumar³

2021

IJIES

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Heart disease is a serious disease that causes sudden death among 80% of the people around the world. The traditional models performed predictive analytics using machine learning techniques to make a better decision. For better decision making in heart disease prediction, the big data analysis shows the great opportunities to predict the future health status from health parameters and provide best outcomes. However, the traditional decision making models had traffic data or contained noise and uncertainty was unpredictable as the data ambiguity emerged. In order to overcome such an issue, the big data is used to ensure the medical service which is mostly needed in a timely manner and for accurate diagnosis. The pre-processing of the medical data acquired from Cleveland heart disease UCI datasets has a vast number of attributes which helps to predict the heart disease. The data are contaminated with the noise and some of the data are missing, so the pre-processing using Min max Normalization is performed to remove contaminated noise acquired in the data which is taken from the UCI repository dataset. The proposed Fuzzy Deep Convolution Network (FDCN) permits the input features for fuzzification process that uses transformed features. The fuzzification process eliminates the redundant or irrelevant fuzzified features and overcomes the system complexity problems. The proposed FDCN obtains accuracy of 95.56 % and 92 % of F-score shows better results when compared with the existing KNN-DT, Naive Bayes, and Random Forest algorithms.

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“…Swain et al (Swain et al, 2020) presented a study to find the best classifier algorithm that helps the nonspecialized doctors or medical technicians in the way of diagnosing and predicting the risk of heart diseases. The study focused on using different machine learning algorithms like LR, SVM, KNN, NB, DT, and RF classifiers.…”

Section: Heart Rate Diseasementioning

confidence: 99%

Performance Evaluation of Different Machine Learning Classification Algorithms for Disease Diagnosis

Al-Hashem

Alqudah

Qananwah

2021

International Journal of E-Health and Medical Communications

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Knowledge extraction within a healthcare field is a very challenging task since we are having many problems such as noise and imbalanced datasets. They are obtained from clinical studies where uncertainty and variability are popular. Lately, a wide number of machine learning algorithms are considered and evaluated to check their validity of being used in the medical field. Usually, the classification algorithms are compared against medical experts who are specialized in certain disease diagnoses and provide an effective methodological evaluation of classifiers by applying performance metrics. The performance metrics contain four criteria: accuracy, sensitivity, and specificity forming the confusion matrix of each used algorithm. We have utilized eight different well-known machine learning algorithms to evaluate their performances in six different medical datasets. Based on the experimental results we conclude that the XGBoost and K-Nearest Neighbor classifiers were the best overall among the used datasets and signs can be used for diagnosing various diseases.

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An Efficient Heart Disease Prediction System Using Machine Learning

Cited by 19 publications

References 3 publications

Yoga Pose Monitoring System using Deep Learning

Yoga Pose Monitoring System using Deep Learning

A Big Data Analysis Using Fuzzy Deep Convolution Network Based Model for Heart Disease Classification

Performance Evaluation of Different Machine Learning Classification Algorithms for Disease Diagnosis

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