Prediction of Anemia using Machine Learning Algorithms

Dhakal, Prakriti

doi:10.5121/ijcsit.2023.15102

Cited by 13 publications

(7 citation statements)

References 3 publications

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“…The findings of this study show that machine learning algorithms may be used to predict rainfall in Australia. Random Forest and Extreme Gradient Boosting outperformed the other machine learning models examined, with the lowest mean squared error and highest R-squared values [18]. This implies that decision tree-based algorithms are especially adept at dealing with the complicated interactions between meteorological factors and rainfall.…”

Section: Discussionmentioning

confidence: 90%

Rainfall Prediction using XGB Model with the Australian Dataset

Vinta,

Peeriga

2024

EAI Endorsed Trans Energy Web

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Rainfall prediction is a critical field of study with several practical uses, including agriculture, water management, and disaster preparedness. In this work, we examine the performance of several machine learning models in forecasting rainfall using a dataset of Australian rainfall observations from Kaggle. Six models are compared: random forest (RF), logistic regression (LogReg), Gaussian Naive Bayes (GNB), k-nearest neighbours (kNN), support vector classifier (SVC), and XGBoost (XGB). Missing value imputation and feature selection were used to preprocess the dataset. To analyse the models, we employed cross-validation and performance indicators such as accuracy, precision, recall, and F1-score. According to our findings, the RF and XGB models fared the best, with accuracy ratings of 87% and 85%, respectively. With accuracy ratings below 70%, the GNB and SVC models performed the poorest. Our findings imply that machine learning algorithms can be useful tools for predicting rainfall, but careful model selection and evaluation are required for reliable results.

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Section: Discussionmentioning

confidence: 90%

Rainfall Prediction using XGB Model with the Australian Dataset

Vinta,

Peeriga

2024

EAI Endorsed Trans Energy Web

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“…This paper [5] investigates the use of machine learning algorithms to predict anemia in children. It compares the performance of different algorithms and finds that Random Forest is the most effective, achieving an accuracy of 98.4%.…”

Section: Literature Reviewmentioning

confidence: 99%

Review Paper Anemia Detection using ML- A Comparative Study

D N

2024

IJSREM

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Anemia's global grip calls for a groundbreaking solution. We propose a comprehensive machine learning model integrating both traditional (symptoms, history, vitals) and novel data: smartphone-captured conjunctival images. Deep learning extracts hidden visual cues from these images, revealing unseen anemic signatures. This data fusion, unlike analyzing each source alone, creates a multi-faceted risk assessment, boosting accuracy and robustness. But generalizability is key. Rigorous testing across diverse populations ensures real-world effectiveness for everyone. Early detection unlocks the promise of mitigating anemia's impact. Prompt interventions and optimized treatment plans can dramatically improve lives, ushering in a new era of preventive healthcare. This project aligns with global efforts to empower individuals and fight anemia worldwide. Join us as we reshape anemia diagnosis, one image and data point at a time. Keywords : Anemia , Machine Learning, Structured data, Unstructured data, Convolution Neural Network.

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“…Through the integration of theoretical understandings and empirical data, this chapter provides an in-depth analysis of airline market dynamics and the implications of regulatory changes on the industry's environment. Gupta and Kharbanda's [5] study investigates the use of machine learning algorithms to forecast flight delays, making use of past flight data to create accurate prediction models. A range of machine learning methods, including random forests, decision trees, and support vector machines, are applied to a dataset that includes flight data obtained from multiple sources.…”

Section: Literature Reviewmentioning

confidence: 99%

Airtime Analytics: Using Machine Learning To Forecast Flight Delays

Raveshia,

Jain

2024

Preprint

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Airlines must manage flight schedules adequately to maintain consumer satisfaction and profitability in operations. This study aims to create a prediction model for aeroplane delays by applying machine learning techniques. This research aims to evaluate the potential of multiple machine learning algorithms in predicting flight delays using a large dataset that includes precise aircraft specifications, airline operating data, and records of previous delays. Throughout-depth pretreatment techniques are used in the study to ensure data accuracy, and feature selection is carried out to pinpoint important delay variables. A variety of measures are used to assess the model, and the decision tree regression model’s evaluation metrics show overall high performance. The R-squared score of 0.933 indicates that the features of the model account for around 93.3% of the variance in the target variable, exhibiting an outstanding capacity to capture data variability. The low values comprising a Mean Squared Error of 102.71 are also observed.

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Prediction of Anemia using Machine Learning Algorithms

Cited by 13 publications

References 3 publications

Rainfall Prediction using XGB Model with the Australian Dataset

Rainfall Prediction using XGB Model with the Australian Dataset

Review Paper Anemia Detection using ML- A Comparative Study

Airtime Analytics: Using Machine Learning To Forecast Flight Delays

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