Advanced Machine Learning Models in Prediction of Medical Conditions (original) (raw)

Comparative Analysis of ML models on Multiple Diseases

IJRAR, 2023

Data mining can extract essential information from unstructured data. With the continuous growth and expansion of the healthcare sector, the need for creating successful decision support systems for medical applications is becoming increasingly important. This article offers a comparative examination of different machine learning (ML) models to forecast multiple illnesses. The investigation employs a dataset containing health records of patients, encompassing demographic, clinical, and laboratory factors, to assess the effectiveness of distinct ML models. Prompt decision-making is crucial in the diagnosis of diseases. This paper examines a study comparing various machine learning models for forecasting diseases using different attributes and a more effective algorithm. The research offers recommendations that could greatly benefit the healthcare industry, and the project's actualization provides practical insights. The paper concludes with a summary of the authors' contributions to the topic, addressing any shortcomings and suggesting areas for future improvement. This study will be useful for physicians and researchers in identifying critical traits for disease identification. The study results can help with the selection of suitable ML models for predicting diseases and offer valuable insights into the advantages and disadvantages of various models.

Application of Machine Learning in Disease Prediction

International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2023

Healthcare is a sector that is always changing. Healthcare professionals may find it challenging to stay current with the constant development of new technologies and treatments. As a result, the purpose of this research paper is to try and implement machine learning features in a specific system for health facilities. Knowing if we are ill at an early stage rather than finding out later is crucial. The entire process of treatment can be made much more effective if the disease is predicted ahead using specific machine learning algorithms as opposed to directly treating the patient. In this work, disease is predicted based on symptoms using machine learning. Machine learning algorithms like Naive Bayes, Decision Tree, Random Forest, and KNN are used to forecast the disease on the provided dataset. As you can see, there are numerous potential applications of machine learning in clinical care in the areas of patient data improvement, diagnosis, and treatment, cost reduction, and improved patient safety.

The Significance of Machine Learning in Clinical Disease Diagnosis: A Review

The global need for effective disease diagnosis remains substantial, given the complexities of various disease mechanisms and diverse patient symptoms. To tackle these challenges, researchers, physicians, and patients are turning to machine learning (ML), an artificial intelligence (AI) discipline, to develop solutions. By leveraging sophisticated ML and AI methods, healthcare stakeholders gain enhanced diagnostic and treatment capabilities. However, there is a scarcity of research focused on ML algorithms for enhancing the accuracy and computational efficiency. This research investigates the capacity of machine learning algorithms to improve the transmission of heart rate data in time series healthcare metrics, concentrating particularly on optimizing accuracy and efficiency. By exploring various ML algorithms used in healthcare applications, the review presents the latest trends and approaches in ML-based disease diagnosis (MLBDD). The factors under consideration include the algorithm utilized, the types of diseases targeted, the data types employed, the applications, and the evaluation metrics. This review aims to shed light on the prospects of ML in healthcare, particularly in disease diagnosis. By analyzing the current literature, the study provides insights into state-of-the-art methodologies and their performance metrics.

Machine Learning Methods Used in Disease Prediction

2018

Advances in machine learning allow us to predict certain events before they happen. Diseases and deaths are one of the most painful of those events for people all around the world. There are huge amounts of health data available that can be used for machine learning to predict diseases that are going to be seen in a person. Sometimes it is possible to prevent diseases and even deaths if a patient takes precaution against it. So, it is possible to save millions of lives through predicting and preventing diseases and deaths using machine learning. In this paper, the concept of preventable diseases and deaths will be discussed. Then, studies that have been done in this field will be analyzed. In the end, future potential and enablers of disease prediction will be examined.

Disease Prediction using Machine Learning

international journal for research in applied science and engineering technology ijraset, 2020

Machine learning has various applications and one of them is healthcare. There should be much more advanced medical facilities so as to provide the best possible treatment for the patients[3].Also there are many machine learning algorithms (such as KNN, Random Forest and Decision Tree Classifier algorithms and many more) which were selected and on the given data many algorithms were applied so as to produce the best results. We can say that when machine learning implemented in healthcare can lead to a high increase in patient satisfaction. so this research paper, will try to implement functions of machine learning in health facilities in a particular system[8]. Instead of directly performing treatment for the patient, if the disease is predicted beforehand using certain machine learning algorithms then the entire process of treatment can be made much more efficient[12]. There are also some cases which occur when early diagnosis of a disease is not performed or carried out. Hence disease prediction is a really important step while treating the patient. As it is said "Prevention is better than cure", the right prediction of disease would definitely lead to an early prevention of that particular disease[19].

A Review Article on the Prediction of Diseases at an Early Stage

International Journal of Innovative Research in Computer Science and Technology (IJIRCST), 2023

Individuals today suffer from a wide range of diseases as a result of their lifestyle choices and the environment in which they live. The objective of forecasting disease at an earlier stage becomes an increasingly vital condition as the identification and prediction of such diseases at their earlier phases become highly significant. Most individuals are too lazy to go to the hospital or see a doctor for a small problem. Our approach focuses on accuracy to detect additional symptoms for illness prediction in healthcare. In this section, I've employed a variety of machine learning algorithms carefully and focused in this few, which achieved the highest accuracy with that specific condition in order to build a strong model that produces the most exact forecasts. This work introduces the topics of illness prediction, disease therapy, and local medical consultation with effective machine learning programming. There are several diseases in the world that are brought on by the conditions of people's living habits or their surroundings. Thus, this study offers a summary of machine learning-based illness prediction.

A comprehensive study on disease risk predictions in machine learning

International Journal of Electrical and Computer Engineering (IJECE), 2020

Over recent years, multiple disease risk prediction models have been developed. These models use various patient characteristics to estimate the probability of outcomes over a certain period of time and hold the potential to improve decision making and individualize care. Discovering hidden patterns and interactions from medical databases with growing evaluation of the disease prediction model has become crucial. It needs many trials in traditional clinical findings that could complicate disease prediction. A Comprehensive study on different strategies used to predict disease is conferred in this paper. Applying these techniques to healthcare data, has improvement of risk prediction models to find out the patients who would get benefit from disease management programs to reduce hospital readmission and healthcare cost, but the results of these endeavors have been shifted.

Chronic Disease Prediction Using Machine Learning

International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022

Technological advancement, including machine learning, has a significant impact on health by allowing for more accurate diagnosis and treatment of various chronic diseases. Accurate prediction is critical in the biomedical and healthcare communities for determining the risk of disease in patients. The only way to overcome chronic disease mortality is to predict it earlier so that disease prevention can be implemented. Such a model is a Patient's requirement for which Machine Learning is highly recommended. However, a doctor finds it difficult to make an exact forecast based just on symptoms. The most challenging task is making an accurate diagnosis of a disease. Data mining is crucial in helping to predict the sickness and solve this issue. Based on a dataset for chronic diseases from the UCI machine learning data warehouse, this study assesses chronic diseases using machine learning techniques. In order to create accurate prediction models for various chronic diseases using data mining approaches, we employ datasets for heart disease, kidney disease, cancer disease, and diabetes disease. To increase accuracy and shorten training time, the dataset's most pertinent features are chosen. The system evaluates the user's symptoms as input and outputs the likelihood that the disease will occur. The implementation of Logistic Regression is used to predict disease. Prediction of diseases like diabetes, heart disease, cancer, and kidney disease using logistic regression, random forest, and decision trees are performed. Different models, methodologies, and algorithms are utilized to forecast and analyses each chronic disease. The study includes a conceptual model that includes the prediction of the majority of chronic diseases.

A Review on Machine Learning Techniques to Predict Diseases

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2019

In Disease Diagnosis, affirmation of models is so basic for perceiving the disease exactly. Machine learning is the field, which is used for building the models that can predict the yield relies upon the wellsprings of data, which are connected subject to the past data. Disease unmistakable verification is the most essential task for treating any disease. Classification computations are used for orchestrating the disease. There are a couple of classification computations and dimensionality decline counts used. Machine Learning empowers the PCs to learn without being changed remotely. By using the Classification Algorithm, a hypothesis can be looked over the course of action of decisions the best fits a game plan of recognition. Machine Learning is used for the high dimensional and the multi-dimensional data. Better and modified computations can be made using Machine Learning.

Medical Disease Prediction using Machine Learning Algorithms

International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022

There is a growing importance of healthcare and pandemic has proved that healthcare is an important aspect of an individual life. Most of the medical diagnoses require going to the doctor and fixing appointments for a consultation and sometimes to get accurate disease indications we have to wait for blood reports also we have to travel long distances to seek doctor consultation. When we are not feeling well the first thing we do is to check our temperature to get an estimate or baseline idea of our fever so we can consult our doctor if the temperature is high enough similarly a medical disease prediction application can be used to get a baseline idea of disease and can indicate us whether we should take immediate doctor consultation or not, or at least start some home-remedies for the same to find temporary relief. Combining machine learning with an application interface to interact with users provides opportunities for easy interaction with the users with the machine learning model to get more accurate predictions. Sometimes people feel reluctant to visit a hospital or consult a doctor for minor symptoms but there are cases where these minor symptoms may be indications of severe health problems hence medical disease prediction maybe useful to get a baseline prediction or estimation of disease in such cases.