Heart Disease Prediction Using Machine Learning Algorithm

Rengarajan, A.; Balakrishnan, S.

doi:10.35940/ijitee.j9340.0881019

Cited by 13 publications

(3 citation statements)

References 12 publications

(5 reference statements)

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“…The agenda of the proposed technique is to restore a correlation with trained datasets in extracting and evaluating the samples of speech and classifying on demand. These speech signals are interdependent and have a higher order of distinction in recovering and validating the sample of COVID-19 patients' mental stability and sentiments ( 20 ).…”

Section: Methodsmentioning

confidence: 99%

Neural Network Based Mental Depression Identification and Sentiments Classification Technique From Speech Signals: A COVID-19 Focused Pandemic Study

Ahmed

Singh

Basha

et al. 2021

Front. Public Health

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COVID-19 (SARS-CoV-2) was declared as a global pandemic by the World Health Organization (WHO) in February 2020. This led to previously unforeseen measures that aimed to curb its spread, such as the lockdown of cities, districts, and international travel. Various researchers and institutions have focused on multidimensional opportunities and solutions in encountering the COVID-19 pandemic. This study focuses on mental health and sentiment validations caused by the global lockdowns across the countries, resulting in a mental disability among individuals. This paper discusses a technique for identifying the mental state of an individual by sentiment analysis of feelings such as anxiety, depression, and loneliness caused by isolation and pauses to the normal chains of operations in daily life. The research uses a Neural Network (NN) to resolve and extract patterns and validate threshold trained datasets for decision making. This technique was used to validate 2,173 global speech samples, and the resulting accuracy of mental state and sentiments are identified with 93.5% accuracy in classifying the behavioral patterns of patients suffering from COVID-19 and pandemic-influenced depression.

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Section: Methodsmentioning

confidence: 99%

Neural Network Based Mental Depression Identification and Sentiments Classification Technique From Speech Signals: A COVID-19 Focused Pandemic Study

Ahmed

Singh

Basha

et al. 2021

Front. Public Health

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“…These machine learning paradigms have been applied to heart failure classification by numerous researchers. Some researchers used data mining and Map Reduce algorithms [3][4][5][6]. Some of the researchers applied various machine learning algorithms to create a predictive model for heart failure classification [7][8][9][10][11][12].…”

Section: Machine Learning: a Synopsismentioning

confidence: 99%

Evaluative Study of Machine Learning Classifiers in Predicting Heart Failure: A Focus on Imbalanced Datasets

Ravulapalli,

Paladugu,

Likki

et al. 2023

ISI

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Heart disease persistently remains a paramount health concern globally, necessitating early and precise detection for effective therapeutic intervention, particularly within the realm of cardiology. This study proposes a predictive model for heart failure, utilizing six distinct machine learning classification algorithms-Stochastic Gradient Descent (SGD), Logistic Regression (LR), Decision Tree (DT), AdaBoost, Support Vector Machine (SVM), and Random Forest (RF)-and assesses their performance on an imbalanced heart failure clinical record dataset obtained from Kaggle. Consisting of 299 observations, the dataset comprises 32.11% of instances resulting in death and 67.89% marking recovery or survival, thereby presenting a significant imbalance. This imbalance potentially contributes to a suboptimal prediction of the non-death instances. To address this issue, the Synthetic Minority Oversampling Technique (SMOTE) is employed. The performance of each classifier is evaluated using measures such as accuracy, precision, recall, and F-score. Experiments are conducted on the complete feature set and a selected subset of features, focusing particularly on highly correlated features. The results from these experiments are then juxtaposed with those derived using the comprehensive feature set. The outcome of these comparative analyses reveals superior performance by the RF algorithm over other tree-based and statistical-based models, thereby achieving enhanced accuracy. This study, therefore, presents an in-depth evaluation of machine learning algorithms in predicting heart disease, contributing significantly to the ongoing research in cardiology and machine learning.

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“…Rajashree Dash, Dr. Pradipta Kishore Dash [3] concluded that output of CEFLANN has higher accurate results compared to SVM, KNN, DT models. Rajesh, N. Srinivas [4] has opined that to obtain the result having maximum efficiency with minimum classification, complicated stock trend need to be normalized in line of probability distributions. P. Lakshmi Prasanna, D. Rajeswara Rao [5] proved that the accuracy of PRO-RNN algorithm did increase opposing the loss of information due to memory inconsistency.…”

Section: Deep Learning Techniques Like Long Short Termmentioning

confidence: 99%

Stock Price Prognosticator using Machine Learning Techniques

Nishitha

Bano

Reddy

et al. 2020

2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA)

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S tock market price prediction is one of the favourite research topics under consideration for professionals from various fields like mathematics, statistics, history, finance, computer science engineering etc., as it requires a set of skills to predict variation of price of shares in a very volatile and challenging share market scenario. S hare market trading is mostly dependent on sentiments of investors and other factors like economic policies, political changes, natural disasters etc., Many theories were forwarded, mathematical and statistical applications in conjunction with probability, to simplify the complex process. After the advent of computers, it got further simplified but still challenging due to various external influential factors ruling the volatility of the market prices. Thus, AI and ML algorithms were being developed, but for only for next day using Linear Regression procedures.Our project aims to predict the prices of shares more precisely and accurately using special algorithms using RNN by improvising the back propagation, feedback routines to overcome the short-term memory loss involved in RNN thus providing efficiency in LS TM applications.Our project emphasizes how the LS TM applications perform with datasets of extreme, larger and minimal fluctuating data.

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Heart Disease Prediction Using Machine Learning Algorithm

Cited by 13 publications

References 12 publications

Neural Network Based Mental Depression Identification and Sentiments Classification Technique From Speech Signals: A COVID-19 Focused Pandemic Study

Neural Network Based Mental Depression Identification and Sentiments Classification Technique From Speech Signals: A COVID-19 Focused Pandemic Study

Evaluative Study of Machine Learning Classifiers in Predicting Heart Failure: A Focus on Imbalanced Datasets

Stock Price Prognosticator using Machine Learning Techniques

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