Abstract
Disease prediction is vital for effective treatment decisions in healthcare organizations. This work focuses on predicting multiple diseases using an improvised Machine Learning concept called 'Apna Clinic.' The system analyzes patient health records to forecast diseases like diabetes, breast cancer, heart disease, kidney disease, and liver disease using data normalization and weighted feature extraction. Comparison with existing models and comprehensive error analysis ensure accurate predictions. The behavior model is stored and deployed via the Flask API, enabling reliable functionality. Users access the system, submit disease parameters, and receive their health status. The analysis helps identify serious diseases, monitor patients, and provide timely warnings or suggestions for treatment. In acute cases, the system locates specialized doctors nearby. Additionally, a disease compendium with information on symptoms, prevention, and treatment is provided. The aim is to enhance treatment decisions, empower individuals, and facilitate proactive healthcare actions.
TopIntroduction
Machine learning (Atitallah et al., 2020; Golas et al., 2018; Reddy & Delen, 2018; Shameer et al., 2018), an emerging method leveraging extensive healthcare data, has proven effective in decision making and prediction. Early detection of a disease plays a pivotal role in enhancing overall health and well-being, as it empowers individuals to proactively address potential health risks before they reach a critical stage (World Health Organization & Public Health Agency of Canada, 2005). By identifying warning signs and risk factors at an early stage, individuals have the opportunity to take preventive measures, seek appropriate medical attention, and make necessary lifestyle changes. This proactive approach significantly increases the chances of successful intervention, treatment, and overall health improvement. By avoiding the detrimental effects of late-stage diseases, individuals can lead healthier lives and reduce the potential impact on their quality of life. Accurate prediction of treatment outcomes is essential for informed decision making, leading to improved patient care and disease management. For instance, in the context of heart disease management, accurately predicting which patients should receive heart disease medication and undergo additional checkups can help avoid the unnecessary administration of such drugs. By identifying those individuals who truly require intervention, resources can be allocated more effectively, ensuring optimal care and minimizing potential side effects or complications associated with unnecessary medication usage.
The existing Artificial Intelligence models for medical diagnostics have typically focused on individual diseases, with separate systems developed for each specific condition (Gopisetti et al., 2023; Harimoorthy & Thangavelu, 2021; Xie et al., 2021). Many studies and applications have been dedicated to developing highly accurate and specialized models for specific diseases such as diabetes diagnosis (Alyoubi et al., 2020), cancer detection (Saba, 2020; Munir et al., 2019), skin disease identification (ALKolifi ALEnezi, 2019; Allugunti, 2022), cardiovascular disease prediction (Li et al., 2020; Chen & Hengjinda, 2021), and others. These disease-specific models serve a vital role in medical diagnostics and decision-making by providing targeted predictions and insights for specific conditions. They have demonstrated impressive performance in their respective domains and have contributed significantly to advancing healthcare practices. However, the drawback of such disease-specific models is the fragmented nature of the information they provide. Users often need to access multiple platforms or systems to obtain predictions for different diseases, which can be time-consuming and inconvenient. There has been a lack of integrated frameworks that enable comprehensive disease predictions within a single platform (Gopisetti et al., 2023).
The proposed system in this study aims to address this limitation by offering a single user interface that consolidates multiple disease predictions. By consolidating various disease prediction models into a unified platform, the proposed system offers a convenient and efficient solution for healthcare professionals and individuals seeking accurate and timely disease predictions. Through this integrated approach, users can access a wide range of disease forecasts, covering conditions such as heart diseases, breast cancer, diabetes, malaria, pneumonia, chronic kidney diseases, liver diseases, and more. The utilization of a single user interface enhances usability and accessibility, simplifying the process of obtaining predictions for multiple diseases.
Key Terms in this Chapter
Convolutional Neural Network: A convolutional neural network (CNN) is a type of deep learning algorithm that is specifically designed for processing and analysing structured grid-like data, such as images or time series data. CNNs have been particularly successful in tasks related to computer vision, including image classification, object detection, and image segmentation. The key feature of CNNs is their ability to automatically learn and extract hierarchical patterns and features from input data. They achieve this by using specialised layers called convolutional layers, which apply filters or kernels to input data to detect local patterns. These filters are learned during the training process and help capture different visual features, such as edges, textures, and shapes, at various levels of abstraction.
Deep Learning: Deep learning is a subfield of machine learning that focuses on training artificial neural networks to learn and make predictions or decisions on their own. It is inspired by the structure and function of the human brain, specifically its interconnected network of neurons. Deep learning algorithms are designed to automatically learn representations of data through multiple layers of interconnected artificial neurons, also known as artificial neural networks. These networks have an input layer, one or more hidden layers, and an output layer. Each layer consists of multiple neurons that perform mathematical computations on the input data.
Flask API: Flask is a back-end micro web framework. It is useful for developers, designed to enable them to create and scale web apps quickly and simply. API means Application Programming Interface.
Mortality: Mortality is defined as the number of deaths in the population in a given period of time.
Python Pickling: Pickling is a process where a python object hierarchy is converted into a byte code or binary format. It is used to save the behaviour of the Machine Learning model.
Machine Learning: Machine Learning is the field of study which gives computers the ability to learn without being explicitly programmed. In our project, we have mainly used Supervised Machine Learning algorithms.
Weighted Normalised Feature Extraction: Feature extraction refers to the process of translating a dataset into features that are able to represent the dataset more effectively and result in a better learning performance. In weighted normalised feature extraction, these features are normalised using normalisation methods and ordered according to their priorities (weights).
Disease Compendium: A detailed description of almost all the known diseases are provided in the form of an encyclopaedia, consisting of symptoms, prevention and cure methods for common people to refer and take actions accordingly.
Locate Doctor: In case the prediction model detects presence of any disease in the patient input data, it will immediately suggest visiting all the nearby doctors specialising in that disease, so that the patient can seek immediate help. This option is also present in the encyclopaedia, where details of each disease is provided.
Random Forest Classifier: A random forest classifier is a machine learning algorithm that is based on the concept of an ensemble learning method known as random forests. It is a supervised learning algorithm used for classification tasks. Random forests are made up of multiple decision trees, where each tree is built on a randomly selected subset of the training data and features. Each decision tree in the random forest independently makes a prediction, and the final prediction is determined by aggregating the predictions from all the trees, typically using majority voting.
Medical Treatment: Medical treatment means the management and care of a patient to combat disease or disorder. Medical treatment includes using prescription medications, or use of a non-prescription drug at prescription strength.