Disease Prediction From Various Symptoms Using Machine Learning

Keniya, Rinkal; Khakharia, Aman; Shah, Vruddhi; Gada, Vrushabh; Manjalkar, Ruchi; Thaker, Tirth; Warang, Mahesh; Mehendale, Ninad

doi:10.2139/ssrn.3661426

Cited by 34 publications

(7 citation statements)

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“…A disease predictor, functioning as a virtual doctor, can predict diseases with minimal human error. In situations like COVID-19 and Ebola, a disease predictor becomes invaluable, offering disease identification without physical contact [15].…”

Section: Related Workmentioning

confidence: 99%

HDRP: Integrated Strategies for Robust Heart Disease Detection using Random Crop LSTM Processing and Regression Models

Kumar,

Thakur,

Mishra

2024

Preprint

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Detecting heart disease is significant for timely intervention and improved patient outcomes. With the rising global burden of cardiovascular disorders, there is an urgent need for accurate and efficient diagnostic tools. Advanced technologies, such as Machine Learning (ML) and Artificial Intelligence (AI), have shown promise in enhancing early detection and risk assessment. Integrating these innovative approaches into clinical practice can significantly contribute to the early identification of heart disease, facilitating timely interventions and personalized patient care. Addressing this need is vital for reducing the impact of heart disease on public health. This research presents a comprehensive examination of heart disease detection models, encompassing neural networks, backpropagation, random crop techniques, Long Short-Term Memory (LSTM), and regression methods. The investigation included a comparative analysis of various models, including Heart Disease Evaluation (HBE), AdaBoost, Support Vector Machine (SVM), Random Forest, and proposed method (HDRP), with a focus on accuracy, error rates, and precision over 5 and 10 epochs. Notably, the HDRP model emerged as a standout performer, consistently demonstrating superior accuracy and the lowest error values, showcasing its efficacy in heart disease prediction. The discussion highlights the unique features of HDRP, attributed to advanced methodologies or distinctive design elements. The study emphasizes the importance of future research to delve into the specific attributes contributing to HDRP's performance, exploring model architecture, optimization strategies, and scalability across diverse datasets.

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Section: Related Workmentioning

confidence: 99%

HDRP: Integrated Strategies for Robust Heart Disease Detection using Random Crop LSTM Processing and Regression Models

Kumar,

Thakur,

Mishra

2024

Preprint

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“…In CNN-MDRP, which focuses on both structured and unstructured data, the accuracy of disease prediction is higher and faster compared to CNN-UDRP, with an accuracy score of 94.80%. Nearly 230 diseases were listed by Keniya et al [32] with over 1000 distinctive symptoms. Various ML algorithms receive as input a person's symptoms, age, and gender.…”

Section: Literature Reviewmentioning

confidence: 99%

Disease prognosis using machine learning algorithms based on new clinical dataset

Çolak¹,

Tümer-Sivri

Pervan-Akman

et al. 2023

Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering

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Today, artificial intelligence-based solutions are produced to facilitate human life in almost every field. The healthcare sector is one of the sectors which took advantage of these solutions. Due to reasons such as the world’s ever-expanding population, ongoing epidemics, and the emergence of new disease types, it is becoming increasingly difficult for a patient to benefit from health services quickly and to make an accurate diagnosis. At this juncture, artificial intelligence reduces the patient density in hospitals, enables patients to access accurate information, and allows medical students to practice by seeing new cases. In this study, a new and reliable dataset was created with disease information obtained from various sources under the supervision of a specialist medical doctor. Then, new patient histories were added to the dataset used in the previous study, the experiments were repeated with the same algorithms, and the accuracy score comparison was presented. The created dataset includes 2006 unique patient histories, 358 symptoms, and 141 diseases and we think it will be a valuable dataset for researchers who make developments using machine learning in the field of healthcare. Various machine learning algorithms have been used in the training process to predict diseases belonging to different branches of medicine, such as diabetes, bronchial asthma, and covid. Besides, Support Vector Machine, Naive Bayes, K-Nearest Neighbors, Multilayer Perceptron, Decision Tree, and Random Forest algorithms, we also studied popular boosting algorithms such as XGBoost and LightGBM. All algorithms were validated with cross-validation and performance comparisons were made with different performance metrics such as accuracy, precision, recall, and f1-score. It is also the first study to achieve an accuracy score of 99.33% with a dataset that involves a greater number of diseases than the datasets used in the studies examined.

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“…The optimized ensemble model achieved an F1-score of 81 and vaccination delicacy of 92 for the five most common conditions. The deep learning and ML models showed differences in prophetic power and complaint classification patterns [2]. The healthcare assiduity has set up that Machine learning (ML) is a useful and accurate decision-making fashion in the data collection produced in large amounts.…”

Section: Literature Reviewmentioning

confidence: 99%

Disease Prediction: Various Symptoms using Machine Learning

-¹,

-²,

-³

2023

IJFMR

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In recent years, the use of machine learning algorithms has become popular in the healthcare industry for predicting diseases. In this paper, we propose a framework for disease prediction that utilizes three popular algorithms, Decision Tree, Random Forest Tree, and Naive Bayes. We have outlined disease prediction framework utilizing different Ml Calculations. The dataset utilized had more than 230 maladies for processing. Based on the side effects, age, sexual orientation of an individual, the conclusion framework gives the yield as the disease that the person may well be enduring from. The weighted Decision Tree calculation gave the finest comes about as compared to the other calculations. The exactness of the weighted Decision Tree calculation for the forecast was 95.17%. Other algorithms i.e. Random Forest Tree and Naive Bayes also gave the exactness of 95%. If a recommendation system can be made for doctors and medicine while using review mining will save a lot of time. In this type of system, the user face problem in understanding the heterogeneous medical vocabulary as the users are laymen. User is confused because a large amount of medical information on different mediums are available. The idea behind this system is to adapt with the special requirements of the health domain related with users.

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Disease Prediction From Various Symptoms Using Machine Learning

Cited by 34 publications

References 0 publications

HDRP: Integrated Strategies for Robust Heart Disease Detection using Random Crop LSTM Processing and Regression Models

HDRP: Integrated Strategies for Robust Heart Disease Detection using Random Crop LSTM Processing and Regression Models

Disease prognosis using machine learning algorithms based on new clinical dataset

Disease Prediction: Various Symptoms using Machine Learning

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