With the exponential growth of healthcare data, disease prediction has become a crucial area of research in medical data analytics. This paper presents an approach that combines data mining techniques and machine learning algorithms to predict diseases accurately and efficiently. The proposed system analyzes patient data such as medical history, symptoms, lifestyle patterns, and clinical test results to identify potential health risks. Data preprocessing and feature selection are performed to eliminate noise and improve prediction accuracy. Machine learning models such as Decision Trees, Random Forest, Naïve Bayes, and Support Vector Machines (SVM) are applied to classify and predict diseases based on extracted features. Comparative analysis demonstrates that hybrid and ensemble models outperform traditional statistical methods in terms of precision and recall. The integration of data mining with machine learning enables the discovery of hidden patterns and correlations within healthcare datasets, supporting early diagnosis and preventive healthcare strategies. This research contributes to the development of intelligent, data-driven medical decision-support systems.

Keywords : Disease Prediction, Data Mining, Machine Learning, Classification Algorithms, Healthcare Analytics, Predictive Modeling

Authors : K. Kantha Raju

Title : Disease Prediction in Data Mining Techinque using machine Learning

Volume/Issue : 2020;2(2 (March - April))

Page No : 16 - 21

Breast cancer remains one of the most common and life-threatening diseases among women worldwide. Early detection and accurate prognosis play a vital role in improving patient survival rates. This paper focuses on the diagnosis and prognosis analysis of breast cancer using machine learning and data mining classification techniques. The proposed approach employs algorithms such as Decision Trees, Support Vector Machines (SVM), Random Forest, and Naïve Bayes to classify tumor types as benign or malignant based on clinical and histopathological data. Data preprocessing, feature selection, and normalization are performed to enhance model accuracy and reduce computational complexity. The models are trained and validated using benchmark datasets such as the Wisconsin Breast Cancer Dataset (WBCD). Comparative analysis demonstrates that ensemble and hybrid models achieve higher prediction accuracy and robustness than traditional methods. The results highlight the potential of machine learning-based classification in assisting medical professionals with early diagnosis, treatment planning, and personalized healthcare.

Keywords : Breast Cancer, Machine Learning, Data Mining, Classification Techniques, Prognosis Analysis, Medical Diagnosis

Authors : T. Chalapathi Rao

Title : Breast Cancer Diagnosis and Prognsis Analysis by using Machine Learning with Data Mining Classification Techniques

Volume/Issue : 2020;2(2 (March - April))

Page No : 9 -15

The rapid increase in urban population has led to significant challenges in effective waste collection and disposal. Traditional waste management systems are often inefficient, resulting in environmental pollution and increased operational costs. This paper presents an Intelligent Waste Management System (IWMS) based on the Internet of Things (IoT) to enhance waste collection efficiency and sustainability. In the proposed system, smart sensors are installed in waste bins to monitor the fill level, temperature, and odor in real time. The collected data is transmitted through wireless networks to a cloud platform, where it is analyzed to optimize waste collection routes and schedules. The system alerts municipal authorities when bins reach a predefined threshold, thereby reducing unnecessary trips and fuel consumption. By integrating IoT with data analytics, the proposed model improves resource utilization, lowers maintenance costs, and promotes a cleaner urban environment. This approach represents a significant step toward developing smart cities with sustainable waste management practices.

Keywords : Internet of Things (IoT), Smart Waste Management, Sensor Networks, Cloud Computing, Smart Cities, Environmental Sustainability

Authors : Brahmaji Godi

Title : An Intelligent Waste Management System using Internet of Things Applications

Volume/Issue : 2020;2(2 (March - April))

Page No : 5 - 8

The rapid expansion of the World Wide Web has led to an enormous increase in online data, creating the need for efficient techniques to extract valuable information. Web mining, a subset of data mining, focuses on discovering meaningful patterns, relationships, and trends from web resources such as web content, structure, and user behavior. This paper presents an overview of the web mining process and its applications using cloud computing. The process includes data collection, preprocessing, pattern discovery, and analysis, all of which are computationally intensive and require scalable infrastructure. Cloud computing provides an ideal platform by offering elastic storage, parallel processing, and cost-efficient resource management for large-scale web mining tasks. Applications of web mining supported by cloud technology include e-commerce personalization, search engine optimization, sentiment analysis, and online recommendation systems. The integration of web mining with cloud computing enhances data accessibility, processing speed, and analytical capabilities, enabling organizations to make smarter, data-driven decisions.

Keywords : Web Mining, Cloud Computing, Data Mining, Pattern Discovery, Big Data Analytics, Information Retrieval

Authors : N P PATNAIK M

Title : Web Mining Process and Applications using Cloud Computing

Volume/Issue : 2020;2(2 (March - April))

Page No : 1 - 4