International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 10 Issue: 05 | May 2023
p-ISSN: 2395-0072
www.irjet.net
Sepsis Prediction Using Machine Learning Kriti Ohri1, Jahnavi Nelluri2, B Nandini3,Haripriya Palthya4, A David5 1Professor, VNR Vignana Jyothi Institute of Engineering & Technology, Hyderabad
2345Under Graduate Student, VNR Vignana Jyothi Institute of Engineering & Technology, Hyderabad
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Abstract - Sepsis is a potentially life-threatening and
changes how the cases are distributed among the various subgroups.
serious condition that occurs when an infection spreads across the body and triggers a widespread inflammatory response. It has a significantly high death rate, especially for patients in the ICU. Early detection and treatment of sepsis is necessary. Machine learning models for sepsis detection can be trained on a variety of data sources, such as electronic health records (EHRs), vital signs, laboratory test results, and demographic information. Exploratory Data analysis was performed using different preprocessing techniques. To classify the disease, the Random Forest Classifier is used and comparison also performed among various Classifiers like MLP, KNN, etc.
Learning representations for the early detection of sepsis with deep neural networks (2021) This study's objective was to contrast the new deep learning methodology's performance and viability with that of the regression approach using traditional temporal feature extraction. Through comparison with hand-crafted, the accuracy, performance, and feature extraction capability of DNNs are improved. Having the goal variable set, access to enough data, and sufficient explanatory power. A computational approach to sepsis detection (2021) Evaluating the Insight algorithm's sensitivity and specificity three hours before a protracted SIRS event in the prediction of sepsis. The examination of correlations between nine widely used vital sign measures yields this prediction. The sensitivity of 90%, specificity of 81%, and quick prediction of this model are its benefits. SIRS criteria are sensitive to sepsis, but it also has a high proportion of false positive results, which is a drawback.
Key Words: (Sepsis Prediction, Random Forest Classifier, vital signs.)
1.INTRODUCTION As a result of an unbalanced body's response to these toxins, sepsis can affect several organ systems. Sepsis is a disease that anyone can get as a result of infections. People with chronic diseases like cancer, diabetes, renal, lung, and kidney disease, as well as pregnant women, are more likely to catch it because of their compromised immune systems. Also at danger are infants under one year old. This illness poses a significant risk to public health because to its high mortality, high cost of care, and morbidity. The outcomes can be improved with early identification and antibiotic therapy. Pneumonia, stomach infections, kidney infections, and bloodstream infections are all factors that contribute to sepsis. Fast heartbeat, low blood pressure, hypothermia (very low body temperature), hyperventilation (rapid breathing), and severe pain or discomfort are all signs of sepsis. IV antibiotics to fight infection, vasoactive medicines to boost blood pressure in individuals with low blood pressure, and IV antibiotics to treat infection make up the treatment for sepsis.
A Deep Learning-Based Sepsis Estimation Scheme (2020) Of the 34 features in the machine learning implementation, seven values were chosen from the six data quantification channels. Physiochemical prediction models are created using SVM classifiers that are decision-tree based. The proposed plan for the validation procedure of LSTM-RNN and SVM classifiers is validated using the ACNN classifier and two more intelligent classifiers. It is very precise, which shows that the model can accurately forecast the start of shock, severe sepsis, and consequences. Lacks sensitivity; ineffective for any databases that contain a variety of data. Machine learning for prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy (2020) Three steps were used in the feature selection process: reviewing previous sepsis screening models, speaking with local subject matter experts, and finally using supervised machine learning known as gradient boosting. Important plots and the fundamental simplicity of the individual trees are benefits of this. Having the goal variable set, access to enough data, and sufficient explanatory power was observed from this study.
2. LITERATURE REVIEW Vital sign-based detection of sepsis in neonates (2023) Sepsis prediction was performed using the Naive Bayes algorithm in a maximum a posteriori framework up to 24 hours before clinical sepsis suspicion. Data on vital signs were continuously and automatically collected for this investigation. Compared to research using manually obtained vital signs, this takes less time and is less prone to data entry mistake. However, due to its therapeutic relevance, this technique restricts the number of positive instances and
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Prediction of Sepsis in the ICU: A Systematic Review (2019) Three steps were taken to pick features: reviewing current models for sepsis screening, consulting with local
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