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All Journal Indonesian Journal of Electrical Engineering and Computer Science
Thotakura Venkata Sai Krishna
QIS College of Engineering and Technology (Autonomous)

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Heart disease prediction using ML through enhanced feature engineering with association and correlation analysis Annemneedi Lakshmanarao; Thotakura Venkata Sai Krishna; Tummala Srinivasa Ravi Kiran; Chinta Venkata Murali krishna; Samsani Ushanag; Nandikolla Supriya
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 2: May 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i2.pp1122-1130

Abstract

Heart disease remains a prevalent and critical health concern globally. This paper addresses the critical task of heart disease prediction through the utilization of advanced machine learning techniques. Our approach focuses on the enhancement of feature engineering by incorporating a novel integration of association and correlation analyses. A heart disease dataset from Kaggle was used for the experiments. Association analysis was applied to the categorical and binary features in the dataset. Correlation analysis was applied to the numerical features in the dataset. Based on the insights from association analysis and correlation analysis, a new dataset was created with combinations of features. Later, newly created features are integrated with the original dataset, and classification algorithms are applied. Five machine learning (ML) classifiers, namely decision tree, k-nearest neighbors (KNN), random forest, XG-Boost, and support vector machine (SVM), were applied to the final dataset and achieved a good accuracy rate for heart disease detection. By systematically exploring associations and relationships with categorical, binary, and numerical features, this paper unveils innovative insights that contribute to a more comprehensive understanding of the heart disease dataset.
A novel ensemble approach for Twitter sentiment classification with ML and LSTM algorithms for real-time tweets analysis Thotakura Venkata Sai Krishna; Tummalapalli Siva Rama Krishna; Srinivas Kalime; Chinta Venkata Murali Krishna; Sadineni Neelima; Raja Rao PBV
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i3.pp1904-1914

Abstract

Social media sentiment classification was an essential consideration in natural language processing (NLP) for evaluating normal people’s perspectives on a given topic. With Twitter’s massive rise in popularity in recent years, the capacity to extract information about public sentiment from tweets became a major focus. This paper not only analyzed public sentiment through data from Twitter but introduced a novel ensemble approach in the methods employed for Twitter sentiment classification. Real-time tweets on various topics, including “covid,” “crime,” “spam,” “flipkart,” “migraine,” and “airlines,” were extracted and thoroughly examined to gain insight into public opinions. Leveraging the Twitter API for real-time tweet extraction, natural language processing techniques were applied to clean the tweet data. Subsequently, we applied several machine learning (ML) algorithms Naïve Bayes, decision tree (DT), random forest (RF), logistic regression (LGR), and deep learning (DL) algorithms recurrent neural network (RNN), LSTM, and GRU individually. Later, we proposed a novel ensemble of ML and DL algorithms for sentiment classification, with a novel emphasis on ensemble techniques and enhanced the accuracy with a significance compared to individual ML or DL model applied. The experimental results demonstrated that our novel ensemble approach achieved high accuracy when compared to existing work.
Co-Authors Annemneedi Lakshmanarao Chinta Venkata Murali krishna Nandikolla Supriya Raja Rao PBV Sadineni Neelima Samsani Ushanag Srinivas Kalime Tummala Srinivasa Ravi Kiran Tummalapalli Siva Rama Krishna