In today's modern world, traditional systems frequently find it difficult to keep up with the need for early disease identification. To effectively address these issues, early diagnoses of diseases become imperative. This work aims to develop a comprehensive platform utilizing machine learning (ML) for predictive analysis and early diagnoses of diseases. By allowing early disease identification and encouraging a holistic approach to well-being, this study aims to maximize the potential of technology to improve public health outcomes. The primary focus is on predicting heart disease, diabetes, and breast cancer using machine learning algorithms. Specifically, the Naïve Bayes (NB) classifier achieves an 85% accuracy in predicting heart disease. Support vector machine (SVM) attains 75% accuracy for diabetes prediction. The logistic regression (LR) algorithm stands out by achieving an exceptional accuracy of 98% in predicting cancer. The results highlight a high level of reliability and platform's potential for eastly diagnosis of disease.
Top1. Introduction
The field of healthcare is dynamic and varied, and it is deeply entwined with our whole wellbeing. Preventive measures, accurate diagnosis, individualized treatment, and community well-being are at the center of it all. As a fundamental defense against possible health risks, preventive medicine places a strong emphasis on immunizations, tests, and awareness programs. The crucial turning point in healthcare from planning to implementation is the diagnosis. Contemporary healthcare employs a diverse array of diagnostic instruments and technologies, encompassing advanced imaging methods, molecular diagnostics, and conventional physical assessments. The foundation for individualized treatment regimens is laid by prompt and accurate diagnosis, guaranteeing that each patient receives therapies catered to their specific needs. In conclusion, healthcare is a dynamic and expanding profession that involves a wide range of services, leading to advanced medical interventions from preventive measures. Its success is dependent on the collaboration of dedicated experts, the integration of technology, and a dedication to enhancing the well-being of individuals and communities. As healthcare advances, the pursuit of innovation, diversity, and equity stays at the forefront, crafting a healthier and more resilient future for all.
According to IDF Diabetes Atlas it is found that, 537 million adults (20-79 years) worldwide are living with diabetes. This number will rise to 643 million by 2030. Around 110 million men and 80 million women have coronary heart disease. With over 2.3 million new cases and 6,85,000 deaths in 2020, breast cancer is most common cancer diagnosed worldwide. In today’s world where we have advanced technologies, combining medical field with computer-technology would be a great asset and will provide multiple benefits. A study (Yousef et al., 2022) gave a holistic overview in the field of healthcare as an application of AI, they showed how machine learning and deep learning can have tremendous impact in the field of medical science. A study (Shrivastava et al., 2021) made use of feature extraction which was based on enhanced texture to classify the CT-Scan images of lungs and made significant contributions in field of healthcare. An exciting prospect can be predicting human disease using some system. It would be useful for both the patients and the doctors, as it will reduce the manual effort. Machine learning emerges as a vital participant in the ever-changing environment of technology, transforming the way we address complicated challenges, particularly in the domain of healthcare. This active discipline of computer science, characterized by recent advances, acts as a potent tool for automating human functions by meticulously evaluating and processing large datasets. Machine learning not only improves efficiency but also has significant potential in solving key concerns such as early identification and accurate diagnosis of diseases such as cancer, heart disease, and diabetes. The importance of early disease diagnosis cannot be emphasized because it has a direct impact on patient outcomes, treatment optimization, and healthcare expense savings. These diseases pose significant public health issues, putting significant strain on both individuals and worldwide healthcare systems. The inherent ability of machine learning to examine large datasets and reveal subtle patterns places it as a beacon of hope in the field of multi-disease prediction. Unlike traditional methods that focus on a single condition at a time, this approach allows for a holistic knowledge of health dynamics by taking into account the linked nature of numerous ailments. Extensive investigation into existing healthcare analysis systems has revealed a common limitation: they focus on particular diseases in isolation. This restricted focus led to the understanding that such methodologies are designed to analyze one condition at a time, preventing a holistic view of an individual's health. Machine learning, on the other hand, represents a paradigm shift, allowing healthcare practitioners to analyze various diseases at the same time. This not only improves diagnostic precision but also opens the door to tailored and proactive healthcare measures, ushering in a paradigm shift toward a more effective and comprehensive healthcare system.