HPPS : Heart Problem Prediction System Using Machine Learning

Adhikari, Nimai Chand Das; Alka, Arpana; Garg, Rajat

doi:10.5121/csit.2017.71803

Cited by 11 publications

(3 citation statements)

References 21 publications

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“…In the proposed model HPPS (Heart Problem Prediction System), we want to give a brief idea about how our system looks like and behaves [33]. Below is the flow chart of our model: Figure 5: Flow Chart of HPPS -Dataset: There is a common database for the patient from where the data will be taken by the model to finalize the algorithm.…”

Section: Proposed Systemmentioning

confidence: 99%

Prevention of Heart Problem Using Artificial Intelligence

Adhikari¹

2018

IJAIA

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Section: Proposed Systemmentioning

confidence: 99%

Prevention of Heart Problem Using Artificial Intelligence

Adhikari¹

2018

IJAIA

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“…Sentiment analysis is a powerful tool for analyzing many issues related to human interaction with the computer [5]. In recent studies, this concept has been extended to sociology, advertising, marketing, and healthcare [6].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Approaches for Classifying the Distribution of Covid-19 Sentiments

Kuyo¹,

Mwalili²,

Okango³

2021

OJS

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Previously, rapid disease detection and prevention was difficult. This is because disease modeling and prediction was dependent on a manually obtained dataset that includes use of survey. With the increased use of social media platforms like Twitter, Facebook, Instagram, etc., data mining and sentiment analysis can help avoid diseases. Sentiment analysis is a powerful tool for analyzing people's perceptions, emotions, value assessments, attitudes, and feelings as expressed in texts. The purpose of this research is to use machine learning techniques to classify and predict the spatial distribution of positive and negative sentiments of Covid-19 pandemic. This study research has employed machine learning to classify spatial distribution of Covid-19 twitter sentiments as positive or negative. The data for this study were geo-tagged tweets concerning COVID-19 which were live streamed using streamR package. The key terms used for streaming the data were: Corona, Covid-19, sanitizer, virus, lockdown, quarantine, and social distance. The classification used Naive Bayes algorithms with ngram approaches. N-Gram model is a probabilistic language model used to predict next item in a sequence in the form (n − 1) order Markov. It relies on the Markov assumption-the probability of a word depends only on the previous word without looking too far into the past. The steps followed in this research include: cleaning and preprocessing the data, text tokenization using n-gram i.e. 1-gram, 2-gram, and 3-gram, tweets were converted or weighted into a matrix of numeric vectors using Term Frequency Inverse-Document. Also, data were divided 80:20 between train and test data. A confusion matrix was utilized to evaluate the classification accuracy, precision, and recall performance of the various algorithms tested. Prediction was done using the best performing Naive Bayes algorithm. The results of this research showed that under Multinomial Naive Bayes, unigram accuracy was 92.02%, bigram accuracy was 97.37%, and trigram accu-

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“…It has been a useful tool for analysing array of problems that are related to human computer interaction. This can be extended to the fields of sociology, advertising, healthcare [15] and marketing, and thus ultimately to the area of social media. Sentiment could be described as subjective nature of an individual, which relates to the "private state" of an individual.…”

Section: Introductionmentioning

confidence: 99%